{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "olympic-monroe",
   "metadata": {},
   "source": [
    "## 习题 3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "thick-diversity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 108 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>Retention</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>0.71</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>1.66</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>2.01</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>2.16</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>2.42</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>2.42</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>2.56</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>2.60</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>3.31</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>3.64</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>3.74</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>3.74</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>4.39</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>4.50</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>5.07</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>5.26</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>8.15</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>8.24</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>2.20</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>2.93</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>3.08</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>3.49</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>4.11</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>4.95</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>5.16</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>5.54</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>5.68</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>6.25</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>7.25</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>7.90</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 5.86</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 6.28</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 6.97</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 7.06</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 7.78</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 9.23</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 9.34</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td> 9.91</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td>13.46</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td>18.40</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td>23.89</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid</td><td>26.39</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 2.71</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 5.43</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 6.38</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 6.38</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 8.32</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 9.04</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td> 9.56</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>10.01</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>10.08</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>10.62</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>13.80</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>15.99</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>17.90</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>18.25</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>19.32</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>19.87</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>21.60</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low</td><td>22.25</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 108 × 3\n",
       "\\begin{tabular}{lll}\n",
       " FeIon & Dose & Retention\\\\\n",
       " <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Fe3 & high & 0.71\\\\\n",
       "\t Fe3 & high & 1.66\\\\\n",
       "\t Fe3 & high & 2.01\\\\\n",
       "\t Fe3 & high & 2.16\\\\\n",
       "\t Fe3 & high & 2.42\\\\\n",
       "\t Fe3 & high & 2.42\\\\\n",
       "\t Fe3 & high & 2.56\\\\\n",
       "\t Fe3 & high & 2.60\\\\\n",
       "\t Fe3 & high & 3.31\\\\\n",
       "\t Fe3 & high & 3.64\\\\\n",
       "\t Fe3 & high & 3.74\\\\\n",
       "\t Fe3 & high & 3.74\\\\\n",
       "\t Fe3 & high & 4.39\\\\\n",
       "\t Fe3 & high & 4.50\\\\\n",
       "\t Fe3 & high & 5.07\\\\\n",
       "\t Fe3 & high & 5.26\\\\\n",
       "\t Fe3 & high & 8.15\\\\\n",
       "\t Fe3 & high & 8.24\\\\\n",
       "\t Fe3 & mid  & 2.20\\\\\n",
       "\t Fe3 & mid  & 2.93\\\\\n",
       "\t Fe3 & mid  & 3.08\\\\\n",
       "\t Fe3 & mid  & 3.49\\\\\n",
       "\t Fe3 & mid  & 4.11\\\\\n",
       "\t Fe3 & mid  & 4.95\\\\\n",
       "\t Fe3 & mid  & 5.16\\\\\n",
       "\t Fe3 & mid  & 5.54\\\\\n",
       "\t Fe3 & mid  & 5.68\\\\\n",
       "\t Fe3 & mid  & 6.25\\\\\n",
       "\t Fe3 & mid  & 7.25\\\\\n",
       "\t Fe3 & mid  & 7.90\\\\\n",
       "\t ⋮ & ⋮ & ⋮\\\\\n",
       "\t Fe2 & mid &  5.86\\\\\n",
       "\t Fe2 & mid &  6.28\\\\\n",
       "\t Fe2 & mid &  6.97\\\\\n",
       "\t Fe2 & mid &  7.06\\\\\n",
       "\t Fe2 & mid &  7.78\\\\\n",
       "\t Fe2 & mid &  9.23\\\\\n",
       "\t Fe2 & mid &  9.34\\\\\n",
       "\t Fe2 & mid &  9.91\\\\\n",
       "\t Fe2 & mid & 13.46\\\\\n",
       "\t Fe2 & mid & 18.40\\\\\n",
       "\t Fe2 & mid & 23.89\\\\\n",
       "\t Fe2 & mid & 26.39\\\\\n",
       "\t Fe2 & low &  2.71\\\\\n",
       "\t Fe2 & low &  5.43\\\\\n",
       "\t Fe2 & low &  6.38\\\\\n",
       "\t Fe2 & low &  6.38\\\\\n",
       "\t Fe2 & low &  8.32\\\\\n",
       "\t Fe2 & low &  9.04\\\\\n",
       "\t Fe2 & low &  9.56\\\\\n",
       "\t Fe2 & low & 10.01\\\\\n",
       "\t Fe2 & low & 10.08\\\\\n",
       "\t Fe2 & low & 10.62\\\\\n",
       "\t Fe2 & low & 13.80\\\\\n",
       "\t Fe2 & low & 15.99\\\\\n",
       "\t Fe2 & low & 17.90\\\\\n",
       "\t Fe2 & low & 18.25\\\\\n",
       "\t Fe2 & low & 19.32\\\\\n",
       "\t Fe2 & low & 19.87\\\\\n",
       "\t Fe2 & low & 21.60\\\\\n",
       "\t Fe2 & low & 22.25\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 108 × 3\n",
       "\n",
       "| FeIon &lt;chr&gt; | Dose &lt;chr&gt; | Retention &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Fe3 | high | 0.71 |\n",
       "| Fe3 | high | 1.66 |\n",
       "| Fe3 | high | 2.01 |\n",
       "| Fe3 | high | 2.16 |\n",
       "| Fe3 | high | 2.42 |\n",
       "| Fe3 | high | 2.42 |\n",
       "| Fe3 | high | 2.56 |\n",
       "| Fe3 | high | 2.60 |\n",
       "| Fe3 | high | 3.31 |\n",
       "| Fe3 | high | 3.64 |\n",
       "| Fe3 | high | 3.74 |\n",
       "| Fe3 | high | 3.74 |\n",
       "| Fe3 | high | 4.39 |\n",
       "| Fe3 | high | 4.50 |\n",
       "| Fe3 | high | 5.07 |\n",
       "| Fe3 | high | 5.26 |\n",
       "| Fe3 | high | 8.15 |\n",
       "| Fe3 | high | 8.24 |\n",
       "| Fe3 | mid  | 2.20 |\n",
       "| Fe3 | mid  | 2.93 |\n",
       "| Fe3 | mid  | 3.08 |\n",
       "| Fe3 | mid  | 3.49 |\n",
       "| Fe3 | mid  | 4.11 |\n",
       "| Fe3 | mid  | 4.95 |\n",
       "| Fe3 | mid  | 5.16 |\n",
       "| Fe3 | mid  | 5.54 |\n",
       "| Fe3 | mid  | 5.68 |\n",
       "| Fe3 | mid  | 6.25 |\n",
       "| Fe3 | mid  | 7.25 |\n",
       "| Fe3 | mid  | 7.90 |\n",
       "| ⋮ | ⋮ | ⋮ |\n",
       "| Fe2 | mid |  5.86 |\n",
       "| Fe2 | mid |  6.28 |\n",
       "| Fe2 | mid |  6.97 |\n",
       "| Fe2 | mid |  7.06 |\n",
       "| Fe2 | mid |  7.78 |\n",
       "| Fe2 | mid |  9.23 |\n",
       "| Fe2 | mid |  9.34 |\n",
       "| Fe2 | mid |  9.91 |\n",
       "| Fe2 | mid | 13.46 |\n",
       "| Fe2 | mid | 18.40 |\n",
       "| Fe2 | mid | 23.89 |\n",
       "| Fe2 | mid | 26.39 |\n",
       "| Fe2 | low |  2.71 |\n",
       "| Fe2 | low |  5.43 |\n",
       "| Fe2 | low |  6.38 |\n",
       "| Fe2 | low |  6.38 |\n",
       "| Fe2 | low |  8.32 |\n",
       "| Fe2 | low |  9.04 |\n",
       "| Fe2 | low |  9.56 |\n",
       "| Fe2 | low | 10.01 |\n",
       "| Fe2 | low | 10.08 |\n",
       "| Fe2 | low | 10.62 |\n",
       "| Fe2 | low | 13.80 |\n",
       "| Fe2 | low | 15.99 |\n",
       "| Fe2 | low | 17.90 |\n",
       "| Fe2 | low | 18.25 |\n",
       "| Fe2 | low | 19.32 |\n",
       "| Fe2 | low | 19.87 |\n",
       "| Fe2 | low | 21.60 |\n",
       "| Fe2 | low | 22.25 |\n",
       "\n"
      ],
      "text/plain": [
       "    FeIon Dose Retention\n",
       "1   Fe3   high 0.71     \n",
       "2   Fe3   high 1.66     \n",
       "3   Fe3   high 2.01     \n",
       "4   Fe3   high 2.16     \n",
       "5   Fe3   high 2.42     \n",
       "6   Fe3   high 2.42     \n",
       "7   Fe3   high 2.56     \n",
       "8   Fe3   high 2.60     \n",
       "9   Fe3   high 3.31     \n",
       "10  Fe3   high 3.64     \n",
       "11  Fe3   high 3.74     \n",
       "12  Fe3   high 3.74     \n",
       "13  Fe3   high 4.39     \n",
       "14  Fe3   high 4.50     \n",
       "15  Fe3   high 5.07     \n",
       "16  Fe3   high 5.26     \n",
       "17  Fe3   high 8.15     \n",
       "18  Fe3   high 8.24     \n",
       "19  Fe3   mid  2.20     \n",
       "20  Fe3   mid  2.93     \n",
       "21  Fe3   mid  3.08     \n",
       "22  Fe3   mid  3.49     \n",
       "23  Fe3   mid  4.11     \n",
       "24  Fe3   mid  4.95     \n",
       "25  Fe3   mid  5.16     \n",
       "26  Fe3   mid  5.54     \n",
       "27  Fe3   mid  5.68     \n",
       "28  Fe3   mid  6.25     \n",
       "29  Fe3   mid  7.25     \n",
       "30  Fe3   mid  7.90     \n",
       "⋮   ⋮     ⋮    ⋮        \n",
       "79  Fe2   mid   5.86    \n",
       "80  Fe2   mid   6.28    \n",
       "81  Fe2   mid   6.97    \n",
       "82  Fe2   mid   7.06    \n",
       "83  Fe2   mid   7.78    \n",
       "84  Fe2   mid   9.23    \n",
       "85  Fe2   mid   9.34    \n",
       "86  Fe2   mid   9.91    \n",
       "87  Fe2   mid  13.46    \n",
       "88  Fe2   mid  18.40    \n",
       "89  Fe2   mid  23.89    \n",
       "90  Fe2   mid  26.39    \n",
       "91  Fe2   low   2.71    \n",
       "92  Fe2   low   5.43    \n",
       "93  Fe2   low   6.38    \n",
       "94  Fe2   low   6.38    \n",
       "95  Fe2   low   8.32    \n",
       "96  Fe2   low   9.04    \n",
       "97  Fe2   low   9.56    \n",
       "98  Fe2   low  10.01    \n",
       "99  Fe2   low  10.08    \n",
       "100 Fe2   low  10.62    \n",
       "101 Fe2   low  13.80    \n",
       "102 Fe2   low  15.99    \n",
       "103 Fe2   low  17.90    \n",
       "104 Fe2   low  18.25    \n",
       "105 Fe2   low  19.32    \n",
       "106 Fe2   low  19.87    \n",
       "107 Fe2   low  21.60    \n",
       "108 Fe2   low  22.25    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read.table(\"./ex_3_6.meaningfulize.txt\", header=TRUE)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "metric-maryland",
   "metadata": {},
   "outputs": [],
   "source": [
    "attach(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "short-lawrence",
   "metadata": {},
   "source": [
    "### (1)\n",
    "\n",
    "![题（1）](https://tva1.sinaimg.cn/large/008i3skNly1gr7f8iaibgj31lo06y41x.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "british-baptist",
   "metadata": {},
   "source": [
    "首先，求出各组合观测值的样本均值、标准差。这里可以利用 aggregate 做分类汇总："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "waiting-destiny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Fe2</td><td>high</td><td> 5.936667</td><td>2.806778</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td> 3.698889</td><td>2.030870</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low </td><td>12.639444</td><td>6.082089</td></tr>\n",
       "\t<tr><td>Fe3</td><td>low </td><td>11.750000</td><td>7.028150</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid </td><td> 9.632222</td><td>6.691215</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td> 8.203889</td><td>5.447386</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " FeIon & Dose & mean & sd\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Fe2 & high &  5.936667 & 2.806778\\\\\n",
       "\t Fe3 & high &  3.698889 & 2.030870\\\\\n",
       "\t Fe2 & low  & 12.639444 & 6.082089\\\\\n",
       "\t Fe3 & low  & 11.750000 & 7.028150\\\\\n",
       "\t Fe2 & mid  &  9.632222 & 6.691215\\\\\n",
       "\t Fe3 & mid  &  8.203889 & 5.447386\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| Fe2 | high |  5.936667 | 2.806778 |\n",
       "| Fe3 | high |  3.698889 | 2.030870 |\n",
       "| Fe2 | low  | 12.639444 | 6.082089 |\n",
       "| Fe3 | low  | 11.750000 | 7.028150 |\n",
       "| Fe2 | mid  |  9.632222 | 6.691215 |\n",
       "| Fe3 | mid  |  8.203889 | 5.447386 |\n",
       "\n"
      ],
      "text/plain": [
       "  FeIon Dose mean      sd      \n",
       "1 Fe2   high  5.936667 2.806778\n",
       "2 Fe3   high  3.698889 2.030870\n",
       "3 Fe2   low  12.639444 6.082089\n",
       "4 Fe3   low  11.750000 7.028150\n",
       "5 Fe2   mid   9.632222 6.691215\n",
       "6 Fe3   mid   8.203889 5.447386"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_means <- aggregate(Retention, by=list(FeIon, Dose), FUN=mean)\n",
    "grouped_sds  <- aggregate(Retention, by=list(FeIon, Dose), FUN=sd)\n",
    "\n",
    "# 下面几行代码将结果整合到一个表格，方便查看：\n",
    "grouped_means_sds <- cbind(grouped_means, grouped_sds[\"x\"])\n",
    "names(grouped_means_sds) <- c(\"FeIon\", \"Dose\", \"mean\", \"sd\")\n",
    "grouped_means_sds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saving-compilation",
   "metadata": {},
   "source": [
    "为方便观察，按 $Fe^{2+}$、$Fe^{3+}$ 并排比较："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "finite-shield",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>|</th><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Fe2</td><td>high</td><td> 5.936667</td><td>2.806778</td><td>|</td><td>Fe3</td><td>high</td><td> 3.698889</td><td>2.030870</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Fe2</td><td>low </td><td>12.639444</td><td>6.082089</td><td>|</td><td>Fe3</td><td>low </td><td>11.750000</td><td>7.028150</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Fe2</td><td>mid </td><td> 9.632222</td><td>6.691215</td><td>|</td><td>Fe3</td><td>mid </td><td> 8.203889</td><td>5.447386</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & FeIon & Dose & mean & sd & \\textbar{} & FeIon & Dose & mean & sd\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Fe2 & high &  5.936667 & 2.806778 & \\textbar{} & Fe3 & high &  3.698889 & 2.030870\\\\\n",
       "\t3 & Fe2 & low  & 12.639444 & 6.082089 & \\textbar{} & Fe3 & low  & 11.750000 & 7.028150\\\\\n",
       "\t5 & Fe2 & mid  &  9.632222 & 6.691215 & \\textbar{} & Fe3 & mid  &  8.203889 & 5.447386\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 9\n",
       "\n",
       "| <!--/--> | FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | | &lt;chr&gt; | FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | Fe2 | high |  5.936667 | 2.806778 | | | Fe3 | high |  3.698889 | 2.030870 |\n",
       "| 3 | Fe2 | low  | 12.639444 | 6.082089 | | | Fe3 | low  | 11.750000 | 7.028150 |\n",
       "| 5 | Fe2 | mid  |  9.632222 | 6.691215 | | | Fe3 | mid  |  8.203889 | 5.447386 |\n",
       "\n"
      ],
      "text/plain": [
       "  FeIon Dose mean      sd       | FeIon Dose mean      sd      \n",
       "1 Fe2   high  5.936667 2.806778 | Fe3   high  3.698889 2.030870\n",
       "3 Fe2   low  12.639444 6.082089 | Fe3   low  11.750000 7.028150\n",
       "5 Fe2   mid   9.632222 6.691215 | Fe3   mid   8.203889 5.447386"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(function() {\n",
    "    `|` <- rep(\"|\", 3);  # 这里为了显示效果取了一个 `|` 变量名，为避免后续麻烦，使用了函数包裹立即执行来隔离环境\n",
    "\n",
    "    cbind(\n",
    "        grouped_means_sds[grouped_means_sds$FeIon==\"Fe2\",], \n",
    "        `|`,\n",
    "        grouped_means_sds[grouped_means_sds$FeIon==\"Fe3\",]\n",
    "    )\n",
    "})()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saved-persian",
   "metadata": {},
   "source": [
    "按剂量低、中、高并排比较："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "critical-substitute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 14</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>|</th><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>|</th><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>3</th><td>Fe2</td><td>low</td><td>12.63944</td><td>6.082089</td><td>|</td><td>Fe2</td><td>mid</td><td>9.632222</td><td>6.691215</td><td>|</td><td>Fe2</td><td>high</td><td>5.936667</td><td>2.806778</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Fe3</td><td>low</td><td>11.75000</td><td>7.028150</td><td>|</td><td>Fe3</td><td>mid</td><td>8.203889</td><td>5.447386</td><td>|</td><td>Fe3</td><td>high</td><td>3.698889</td><td>2.030870</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 14\n",
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & FeIon & Dose & mean & sd & \\textbar{} & FeIon & Dose & mean & sd & \\textbar{} & FeIon & Dose & mean & sd\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t3 & Fe2 & low & 12.63944 & 6.082089 & \\textbar{} & Fe2 & mid & 9.632222 & 6.691215 & \\textbar{} & Fe2 & high & 5.936667 & 2.806778\\\\\n",
       "\t4 & Fe3 & low & 11.75000 & 7.028150 & \\textbar{} & Fe3 & mid & 8.203889 & 5.447386 & \\textbar{} & Fe3 & high & 3.698889 & 2.030870\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 14\n",
       "\n",
       "| <!--/--> | FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | | &lt;chr&gt; | FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | | &lt;chr&gt; | FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 3 | Fe2 | low | 12.63944 | 6.082089 | | | Fe2 | mid | 9.632222 | 6.691215 | | | Fe2 | high | 5.936667 | 2.806778 |\n",
       "| 4 | Fe3 | low | 11.75000 | 7.028150 | | | Fe3 | mid | 8.203889 | 5.447386 | | | Fe3 | high | 3.698889 | 2.030870 |\n",
       "\n"
      ],
      "text/plain": [
       "  FeIon Dose mean     sd       | FeIon Dose mean     sd       | FeIon Dose\n",
       "3 Fe2   low  12.63944 6.082089 | Fe2   mid  9.632222 6.691215 | Fe2   high\n",
       "4 Fe3   low  11.75000 7.028150 | Fe3   mid  8.203889 5.447386 | Fe3   high\n",
       "  mean     sd      \n",
       "3 5.936667 2.806778\n",
       "4 3.698889 2.030870"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(function() {\n",
    "    `|` <- rep(\"|\", 2);  # sep char: 这里为了显示效果取了一个 `|` 变量名，为避免后续麻烦，使用了函数包裹立即执行来隔离环境\n",
    "\n",
    "    cbind(\n",
    "        grouped_means_sds[grouped_means_sds$Dose==\"low\",], \n",
    "        `|`,\n",
    "        grouped_means_sds[grouped_means_sds$Dose==\"mid\",],\n",
    "        `|`,\n",
    "        grouped_means_sds[grouped_means_sds$Dose==\"high\",]\n",
    "    )\n",
    "})()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "revised-thumb",
   "metadata": {},
   "source": [
    "可以画出箱线图来比较："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "presidential-sheep",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q5S3s6sSx6ZvDc5PBm0PCkz1h/wC5X6\nR9WvCQsp+ZwAAQIECCxOoO5OW9cbj6LUKfr1h++5o1hZ1lE/6H4nOW1E67MaAgQaCnS5g3RJ\n2vjwZJeknnM0t1yVEdWJquuRdp/7YcPhu2e6/RpMuzLT3KHBdCYhQIAAAQIEmguM8jEd1Rmr\nM1P+sHn1TEmAwCQKdLmD1N8ea/tvNvO6lCecP2szy5w7un4FWqgec+cxTIAAAQIECBAgQIBA\nywS6epOGljGqDgECBAgQIECAAAECkyDQ1SNIOwT/1ovYAHV0Z/UipjcpAQIECBAgQIAAAQJT\nKNDVDlJd73PyIrZX3cShzi1W2idwWark9MT2bRc1IkCAAAECBK4vUNe8X5nUHZKVCRboagfp\nlGyTpyT/kHw1eUuypXLBlj702VgF6mG+B4y1BlY+iMCemengQWYccJ76UeS7Sd0WdxSlHkL9\ng1GsyDoIECBAoDMC1Tm6UbK+MzVW0YEEutpBqsYemaxI6lbfb0hOTJRuCviHpnvb7X6p8iuS\n+n9w2KWuldwrqVv5j+q78p6sSwcpCAqBKRL4Str6wilqr6YOJjCq/dBgtTMXgZ7AF/I67mcI\n1F3sXm2LECAwFIFds9Q6cnTgUJY++Qt9UZp4+uQ3Uwt7AvZHvgoECLRVoDP7oy4fQepv/Lq2\naO+k2rKhP9IrAQIECBAgQIAAAQIEFiswCR2kusD/jMU23PQECBAgQIAAAQIECBCYK+A5SHNF\nDI9aYI+s8EmjXqn1ESBAgAABAgQWKVDX3T4n2W6R85m8YwKTcASpY+SqO0fgvhl+c/LPc8Yb\nJECAAIF2CtQfhyvbWbUl12pVljCpF+Ffk7Zdu2Sh6V5A3cHunUndGKzurKpMqIAO0oRuWM0i\nQIAAAQJDELhjllmntdcv6UqHBLbeeuuTN2zYcJ8OVVlVCYxNQAdpbPRWTIBAQ4FfZ7q69e6P\nGk5vMgIEhiewcxa94sgjj5xZsWKy+kjXXnvtzNq1a2d222234emNaclf+MIXZo477rjd00Ea\nUw2slkC3BHSQurW91JbANArULb7fOo0N12YCbRXYb7/9Jq6D1Fbr5ajXmWeeuRyLsQwCUyPg\nJg1Ts6k1lAABAgQIECBAgACBhQR0kBYS8jkBAgQIECBAgAABAlMjoIM0NZu6tQ2t06cqCgEC\nBAgQIECgzQL9v1f6r22uq7otQcA1SEvAM+uyCHw+S3n8sizJQiZZoH7M2TjJDdQ2AgQITJDA\n36QtN5+g9sxuyscz8NhkEjtJv0m73jKhbUuzmhcdpOZWphyOQP3P+KXhLNpSJ0TgBmnHBcmB\nyeoJaZNmECBAYGIFcofDd+6zzz4bd9ppJz9sdWQrX3311Vudc84526S6RyYXd6TaQ6umDtLQ\naJd1wbWdHpTUA+yUbgn8ONV1+6ClbbN6KOWOyU5LW4y5CRAgQGBUAs997nO3ucc97jGq1VnP\nEgXOO++8mcc+tg6MKSWgg9SN78E9U81Pb7/99uu6UV21LIE8b6JOC/vB+vXrDyBCgAABAgQI\nECDQDQEdpG5sp+tupnHiiSduM2kP5usG/2C1/NjHPjbz3ve+11G/wfjMRYAAAQIECBAYi4C7\n2I2F3UoJECBAgAABAgQIEGijgCNIbdwq6kSAAAECBFoscPDBB884o6HFG2hO1datu+4M/W3n\njDZIgMBmBHSQNgNjNAECBAgQIDC/wJVXXjn/B8a2VmCbbeoGZQoBAk0EdJCaKJmGAIFxCtRf\nYqclU3/b0XFuBOtuJLBrprp01pT3yfu6Pf0vk5OTSxKFAAECBFouoIPU8g2kegQIzPw2BnUn\nR4VAWwVum4q9Prlx8qdJ3ZL+E8nBSb/UQyWfl7yzP6LLr/vuu69T7Dq0AdeuXTtz2WWXTeKD\nTTu0FVS1SwI6SF3aWupKgAABAm0UeEcqtVfykl7l3p7XeyevTI5Ndk4OSWp8HQk9JllsqQcm\n77DYmYY1/dFHH62DNCzcISy3d1dVjwoZgq1FTqaADtJkbletIkCAAIHRCOyW1dw/qSNHJ/VW\n+Yi8Hp28pjdcL3Wa6L5JfTZIB6nmb/pMtZtnWoUAAQIEBhTQQRoQzmwECBAgQCACt0jqkRln\n9jRW5HVj8rXe8OyXL2Xg8NkjFvH+zzNtXeO0UDkrE9Q1TwoBAgQIDCiggzQgnNkIECBAgEAE\nzk6uTl6UvCKp6zw+lxyeHJX0y/Z589jk2/0Ri3y9MNNXFAIECBAYsoAHxQ4Z2OIJEFiyQP2Q\nc2Syy5KXZAEEll9gQxb57OTlyWeSv0rqmqQ9k68nz0lemJyS3D55Q6IQIECAQIsFHEFq8cZR\nNQIErhOoO4IdnrwtWZsoBNom8OFUqI7uvDT5VDK73K038IW8Pj05d/aH3hMgQIBA+wR0kNq3\nTdSIAAECBLonUKfVVfZI/iCpa5O2SX6erE5cFxQEhQABAl0Q0EHqwlZSRwIECBDoisAFqWjl\nG12psHoSIECAwPUFXIN0fQ9DBAgQIECAAAECBAhMsYAO0hRvfE0nQIAAAQIECBAgQOD6AjpI\n1/cwRIAAAQIECBAgQIDAFAvoIE3xxtd0Ah0RWJ961rNl1nWkvqpJgAABAgQIdFjATRo6vPFU\nncCUCFyedu6d1J3AFAIECBAgQIDAUAUmoYO0XYTumNwsuWlSvzT/Ojkz+WFvOC8KAQIdFtA5\n6vDGU3UCBAgQINAlgS53kKrur02ekey6GfTTM/6pyXc387nRBAgQIECAAAECBAgQ+J1Al69B\n+kBa8TfJEcn9kj9MbpLsmdQRpUOSi5NvJvdIFAIECBAgQIAAAQIECGxRoKtHkHZOq56U/GXy\n+Xla+POMq1PsPp4cmzw2OS1RCBAgQIAAAQIECBAgsFmBrh5Bqgu261qj/9hsy37/wRfz9r6/\nH/SOAIEOCtSPISs7WG9VJkCAAAECBDom0NUOUh0duiR5+ALedYSsTrX7wQLT+ZgAgfYK7JKq\nHZ/s194qqhkBAgQIECAwKQJdPcVuYzbA+5JjksOSTycXJGuSbZK6acPtkscn+yb3ShQCBLop\n0P8hZ0U3q6/WBAgQIECAQJcEutpBKuPXJF9P3p3MdyRpQ8bXNUhPTOqI0yDl0MxUp/M1KXW7\ncYUAAQIECBAgQIAAgQ4LdLmDVOwnJLdJ6s51t0x2Suqhkr/s5eq8LqU8IDPfvuECbtBwOpMR\nGIrA1ltv/e0VK1bsNZSFj3GhmzZtWrFhw4aZtO+ktO/aMVZlKKveuHHj2muvvfaALPyqoazA\nQgkQIECAAIFFCXS9g9Rv7M/yprLcpZ6h1KTUKX+XNpnQNASGJZA/svd/0pOetPW++9ZZpZNV\nzjvvvJm99957p3SQJqphF1100cy73vWuG6VRN0x0kCZq62oMAQIECHRVYFI6SF31V28Cyypw\n5zvfeeYe9/DYr2VFHeLCquOXDtIQ12DRBAgQGI/Ab37zm5k1a+rScKULApdddlkXqjmyOna1\ng7RDhG69CKW1mXb1IqY3KQECBAgQIECAwAACdWr0y1/+8gHmNAuBdgh0tYN0h/CdvAjCullD\n3e5bIUCAAAECBAgQIECAwGYFutpBOiUtekryD8lXk7ckWyp1C3CFAAECBAgQIECAAAECWxTo\nagepGnVkUldsH5G8ITkxUQgQIECAAAECBMYoUDfUudOd7jSz2267jbEWVr0YgSuuuGLm1FNP\nXcwsEz1tlztItWE+lDwmeWMy8VemP//5z5+ZtLt4ZbtNbPnlL385k/OwV01sAzWMAAECBAjM\nL7Dp8MMPd9Og+W1aObZuGqSD9PtN0/UOUrWkri2qh7lWW+rhsBNbTjmlzixUuiSwzTbbbNWl\n+qorAQIECBAgQGDaBSahg1R3qDtj2jek9hMgQIAAAQIECBAgsHQBv24v3dASCBAgQIAAAQIE\nCBCYEIFJOII0IZti4WaceOKJrkFamKk1Uxx33HEzH/jAB9a1pkIqQoAAAQIECBAgsKCADtKC\nRO2ZYPvtt9dBas/mWLAmq1Zdd3+GTQtOaAICBAgQIECAAIHWCDjFrjWbQkUIECBAgAABAgQI\nEBi3gA7SuLeA9RMgQIAAAQIECBAg0BoBHaTWbAoVIUCAAAECBAgQIEBg3AI6SOPeAtZPgAAB\nAgQIECBAgEBrBHSQWrMpVIQAAQIECBAgQIAAgXEL6CCNewtYPwECBAgQIECAAAECrRHQQWrN\nplARAgQIECBAgAABAgTGLaCDNO4tYP0ECBAgQIAAAQIECLRGwINiW7MpVIQAAQIEOipQPzZu\nnFP3G2T4UcntkguTE5IfJgoBAgQItFxAB6nlG0j1CBAgQKDVAjumdr9JHpN8rFfT6hR9Ltm7\nN1wvG5JXJH9fAwoBAgQItFdAB6m920bNCBAgQKCbAh9JtesI0nOTY5NbJ09I3pCcnXwqWWzZ\nJzPcpOFMKxpOZzICBAgQmEdAB2keFKMIECBAgMCAAntkvrsn/zt5V28ZF+T15OTA5LBkkA7S\nJzLfHZOFyqZMUHVQCBAgQGBAgcV2kOoXsRsmc3+d+m3GXTZgHcxGgAABAgQmRaA6KHU9UnVo\n5pY6Be/pc0c2HL5zplvVYNqrM82vGkxnEgIECBDYjEDTu9jVr1bfTq5MLkrqgtPZ+WCGFQIE\nCBAgMK0Cdb1R/YBY+8aTkjskc8sDM+Jnc0c2HK5OV/0YuVAaLs5kBAgQILA5gaZHkI7KAuqQ\nfV1g+vPk2mR2WT17wHsCBAgQIDAlAnXEaH1SN194fXJOUkd63p18JakO012Suv7oAckhiUKA\nAAECLRZo0kHaJfWv86Yfngxy3nSLm69qBAgQIEBgSQJXZO4dkv2Tg5I79V5vmtftkiq1/zw4\nqR8ZP54oBAgQINBigSYdpKtS/2uSy1vcDlUjQIAAAQLjEliXFZ/Ry5G9StS1unV0qcoHkrcl\nv64BhQABAgTaLdDkGqT6h/+TyaAXlrZbQO0IECBAgMDyC/Q7R7Xkuu5I52j5jS2RAAECQxFo\ncgSpVnxS8qbkW73MvWPddzK+nvugECBAgAABAgQIECBAoLMCTTtIz00L6xS7myUPnqe19SRx\nHaR5YIwiQIAAAQIECBAgQKA7Ak07SLftTpPUlAABAgQIECBAgAABAoMJNO0g9Zded+Tp35Wn\nrl+q+W+c3CL5YqIQIECAAAECBAgQIECgswJNbtJQjbtX8qOkntBdF5pW1iT1fIezEjdwCIJC\ngAABAgQIECBAgEC3BZoeQfrHNHPb5HXJI5Mzk+ow1fVIdf2RDlIQFAIECBAgQIAAAQIEui3Q\npINUp9QdkDwxOTqpJ4TfKKkH3r0xOTGpjtIxiUKAAAECBJZToJ4nVD/QNS3XZsL1TSc2HQEC\nBAgQmCvQ5BS7nTJT7aBO7c18dl7/qPe+HiL7L8mhvWEvBAgQIEBgOQXunoXV6d1NU/skhQAB\nAgQIDCzQ5AjSRVn6b5IDk3OTc5LbJ3UUqa5Fuia5eaIQIECAAIHlFjg/C3z2rIXulfcvSj6d\nnJBcnOyRPC65dfLuRCFAgAABAgMLNOkg1cI/mdR1SHV63WeSelBsnV738eRZyWcThQABAgQI\nLLdA3QzovbMWekrevyp5zaxx9fb9Sf2A99Dka4lCgAABAgQGEmhyil0t+H8l30gOTuo0h5ck\nT0++mNwkcf1REBQCBAgQGKrAzln6PZOPzrOWjRlXnaT7zfOZUQQIECBAoLFA0yNIdSpd3Yhh\nm96S62jSV5N9kxMSF8QGQSEwZoEVH/zgB2eOO+64MVfD6psKXHnllU0nNd3/E7g8L2uSQ5LX\n/b9R1/vvHTNUR5EUAgQIECAwsEDTDlJ/BXfLm4OSa5J/Sm6Y6BwFQSEwboFNmzbNfPe73x13\nNayfwDAF6ijRJ5IXJvXD3X8ma5M/SJ6WPCF5UKIQIECAAIGBBZp2kHbMGo5N/qK3pv/Ia53i\ncFpSpzTUzqo6TQoBAgQIEBimQF33ui55z5yV1NGlhydfmDPeIAECBAgQWJRA0w7SO7PU/ZLH\nJnUHu3snVyV/m7wl+XLyf5M2lDoNsM5B3z/5ZuJi3SAoBAgQmBCBDWlH7Xtem9QpdXX0qA6d\n1gPMq+OkECBAgACBJQk06SDVjRwekzwi+Xzy4qTKpuR9SXWYHpKMuoO0a9b57t66z8/ry5K6\nLurbyT5Jv9QzMeq0i6qvQmBiBVasWLHpjW9848xd7nKXiW3jpDVs9erVM0996lMnrVmjaE89\nZqJK3Shot+R/JnVd0tFJdZQUAgQIECAwsECTDtKNs/Ttk9WbWUuNryNLoy5HZYV/mnw82Tup\nHeOXkzov/WFJHT2qjtFrkuo4fSBRCEy0wPbbbz+z4447TnQbJ6lxN7jBDSapOaNqS11j9G/J\ns5Mjkn9N7p9ckfx1cs/k+4lCgAABAgQGEmjSQbooS16TPD55+Zy1rMhw/fx5xpzxwx7cMyv4\ny+Svkn/vrax2mHX+eZ3+d2pv3BvzWjeWeGQySAepOlrV+WpS+nf4azKtaQgQIEBgMIE65buO\nHH02qVOpq3P00uRdSY17flKPoVCGI3Dd2RgPe9jD6pEfE1XWrVu31VVXXbX1Lrvssm6iGpbG\n5I6ZW+dGPtdOWru0h8CwBJp0kGrdb0temdQTzOsfxxsmtQM6PLlNMupzROqc87qbUd3BqF++\nnDcHJ3XjiNmldqTPmj1iEe/rlI3aATcpOzWZyDQECBAgMLBA7Xv2Teq0718m9cNd7ZM+nFyV\n1A9lhyXK8AS+nkU/+cILL1w1vFWMbcl1fvJj07a68dQklnMmsVHaRGAYAk07SHUkZoekfpnb\ntleROo2hjiw9JTm5N25UL+dlRVsltZP8p6T+oX50UucW/XnyhaRf/iRvzu0PLPK16Y62OmuX\nLHLZJidAgACBxQlsl8nrzIX+L+F1ut2ZyQVJlVskP73unf8MS+C3WfCHh7XwMS/3sqy/rrf+\n4JjrYfUECIxZoGkHqToAL0venhyQ3CxZndSO6fJk1OXCrPAjyRFJHb2qXxSrHm9N6h+29yX1\nK1cdATo0eUiiECBAgEC3BepHuZ8kL0g+m/xx8tqkygOS+lHr1TWgECBAgACBQQWadpD6y784\nb07sD4z59elZf+0oH5jUEaO3JHVh7l2TOuJVZX3yd8nxNaAQIECAQOcF/iYtOC55QlJ3La1T\nwOsU588nX0w+mSgECBAgQGBggaYdpJ2zhocmf5XsMc/aqtP0ynnGD3NUXUT5ql5mr+dPMlDX\nKN0u+U4yMae+1S2BcyvnNGlySi4anbniiism8s5rl1566eRsKC0h0B6BE1KV2g/dMjkr2ZRU\nuW/yteve+Q8BAgQIEFiCQNMO0ruyjsclpyfV4ejvkPL2unJl/01LXn+eelQmpdTta2cOPbTO\nFlS6JLBq1aq1XaqvuhLoiECdUv29OXXVOZoDYnDRAnU9tVM0F81mBgKTJ9Ckg7R9mv3I5BnJ\nkZNH0IkWfSu13D1psr060aBZlawjk29IDpw1bmLerl+/vi76VQgQIECg/QK/SBXrGmaFAIEp\nF2jyB3fdLajuGHf+lFuNu/kTc6rgHMjqQNRNQPp3oZrzsUECBAgQIECAAAECoxOojs9Cpa71\nOSp5TrLTQhP7nAABAgQIECBAgAABAl0VaHIEqdr23OScpO5iV+d5z736/NSMq1uAKwQIECBA\ngAABAgQIEOisQNMOUh1BulWyJqmjSHOPJJ2bcQqBQQTqhh9zb/oxyHLMQ4AAAQIECBAgQGDJ\nAk06SHWThocldWeXij9mg6Asm0A9u6SeZ6IQIECAAIFxChyclb8jmcibBo0T1roJdE2gyTVI\n1abqFH2u91rDCoHlEvhNFlQPd1QIECBAgMA4BXbLym8yzgpYNwEC7RBo0kG6OlU9PnlqsqId\n1VYLAgQIECBAgAABAgQILL9Ak1Psaq0nJm9O7pl8N7kwmX2q3Xcy/JFEIUCAAAECBAgQIECA\nQGcFmnaQnpMW1vNq6mGlfzZPa3fMOB2keWCMIkCAAIGpFqgfFvdJjplqBY0nQIBAhwSadpBu\n26E2qWq3BLZKdeucbw+K7dZ2U1sCBJoJPDiT1Y2OdJCaeZmKAAECYxdo2kEae0VVYGIFHpqW\nvSu55cS2UMMIEJhkgbrj2ZY6PzfN53WWRZ2eXqXu3PnC6975DwECBAi0UkAHqZWbZaoqtW1a\nu91UtVhjCRCYJIF6gHodCb99Ug9N/3EyuxyUgVXJGb2R5/deF/ty38ywZ8OZVjaczmTXF/hp\nBr91/VGGCBCYRgEdpGnc6tpMgAABAsslUKcH3zWpGxk9OakHq78/6ZfX5k2dYvfE/ogBX1+a\n+epoVZNSt6tWFi/wX5nlQYufzRwECEyagA7SpG1R7SFAgACBUQvU4zD+NqlHYhyZPDSpR2P8\nKlmu0vQP941Z4UXLtVLLIUCAwDQK1GkBCgECBAgQILB0gROyiDrK89ukrjl6VKIQIECAQMcE\nHEHq2AZTXQIECBBotcAlqd0jkqclH06uSBzRCYJCgACBrgg4gtSVLaWeBAgQINAlgSNS2bpB\nwzeS73Wp4upKgACBaRfQQZr2b8D4219/OHx0/NVQAwIECCy7QN3R7iHJ45Z9yRY4DIF65uMb\nhrFgyyRAoFsCOkjd2l6TWNuz06jnTmLDtIkAAQIEOiVQR/zq5hoKAQJTLqCDNOVfAM0nQIAA\nAQIECBAgQOD3AjpIv7fwjgABAgQIECBAgACBKRfQQZryL4DmEyBAgAABAgQIECDwewEdpN9b\neEeAAAECBAgQIECAwJQLeA7SlH8BWtD8u6YOL0ke1YK6qAIBAgQIECCwdIGNL37xi9evXLly\n49IXZQmjENi4cWMdNFmV2GZB0EEaxbfOOrYksE8+/OMtTeAzAgQIECAwAoH1Wce6Eaxn4lex\nadOm+19zzTU3ndCG1u3gfzihbftN2rVmQtu2qGbpIC2Ky8QECBAgQIDAhAr8e9r17Qlt26ib\n9dVRr3BE69s56zk2OSA5a0TrtJoxCLgGaQzoVkmAAAECBAi0TmBDavST1tVKhdoksLJXGX8/\nt2mrDKEuNvAQUC2SAAECBAgQIECAAIFuCuggdXO7qTUBAgQIECBAgAABAkMQ0EEaAqpFEiBA\ngAABAgQIECDQTQEdpG5ut0mq9WVpzNpJapC2ECBAgEAnBW6YWt+7kzVXaQIEllVAB2lZOS1s\nAIHPZ566G4xCgAABAgTGKfCgrPy4cVbAugkQaIeADlI7tsO016KePaEQIECAAIFxCtTfRP4u\nGucWaP+664yX1ybntb+qargUAc9BWoqeeQkQIECAAAECBKZFYGMa+oppaew0t9MvJdO89bWd\nAAECBAgQIECAAIHrCeggXY/DAAECBAgQIECAAAEC0yyggzTNW78dbb9pqvGkdlRFLQgQIECA\nAAECBKZdQAdp2r8B42///VKFN4+/GmpAgAABAgQIECBAYDLu1rLrnA15nww/M3lYcuM5nxkk\nQIAAAQIECMwncHJGvnq+D4wj0BPYPq+/SPYkMtkCXb6L3W2zaV6fVCfoT5Odkk8kByf9silv\nnpe8sz/CKwECBAgQIEBgHoH6w/d984w3ikBfoDpIN092SX7WH+l18gS63EF6RzbHXslLepvl\n7XmtJ2C/Mjk22Tk5JKnxFyfHJAoBAgQIECBAgAABAgQ2K9DVDtJuadH9kzpydFKvdY/I69HJ\na3rD9XJasm9Snw3SQfqLzHfLpElZ1WQi0xAgQIAAAQIECBAg0F6BrnaQbhHSusHEmT3aFXmt\nh3d9rTc8++VLGTh89ohFvH96pt2/4fR1xEohMFaB1atXz+y4445jrcMwVn7xxRfP7L777sNY\n9FiX+Ytf1Bk9CgECBAgQINAmga52kM4O4tXJi5J6onFda/S55PDkqKRf6lzRxybf7o9Y5Ov/\naDh9dc4uaThtFyZ7dyr5gBFVdIesp2608YMRra+21ZOT/xrR+ka2mhUrVqx961vf6sYkIxNf\nnhVlu12zadOma5ZnaZZCgAABAgQILFWgqx2kDWn4s5Mjkzsn/5jUNUn/mnw9OTrZJjks2Tt5\nQqI0F/hoJv1u88mXNOV2mbu20feXtJTmM1cHaVSdsea1WoYpN27ceLMspr73k1ZulAb9PLl7\nctakNS6do/VpU0UhQGC8Agdn9fW3xIHjrYa1EyAwboGudpDK7cPJhclLk08ls8vdegNfyGud\nJnfu7A+9X1Dg5ExRUbolUD8cVCatVKf2nOQXyVWT1jjtIUCgNQJ1ffNNWlMbFWmjQO2Dan+0\npo2VU6flE+hyB6kU6rS6yh7JHyR1bVL9gl6/Nq9OfpkoBAh0W6BOP9uv201QewIECBCYAAH7\nownYiE2a0PUOUr+NF+RN5Rv9EV4JECBAgAABAgQIECCwWIG6E5xCgAABAgQIECBAgAABAhHQ\nQfI1IECAAAECBAgQIECAQE9AB8lXgQABAgQIECBAgAABAj0BHSRfBQIE2i6wMhWsW+9O3hNw\n2y6vfgSmS+Cnae63pqvJWrtIAfujRYJ1dXIdpK5uOfUmMD0CO6epz01uNT1N1tIJEai7qj4w\n+evEs3Xav1H/K1V8UPurqYZjFLA/GiP+KFc9KXexG6WZdREgQIAAgdkC9WPjy5KHJb9KXpec\nl3wvuWlSZVPy5uQlNaAQIECAQHsFHEFq77ZRMwIECBDohsDbU83XJJcn90z+PTkiqcdPPCa5\ne/LB5O+S/5EoBAgQINBiAUeQWrxxVI0AAQIEWi+wfWr4zOTxyTHJDZJPJnU06Y+SU5Iqpye3\nTQ5PjksWW2r5+zScabuG05mMAAECBOYR0EGaB8UoAgQIECDQUKCuLaoLt6tTVOWqpDpKd0v6\nnaO8va58Iv/9n733i325T2bYr+FM1WlTCBAgQGBAAR2kAeHMRoAAAQIEIlCn0dXp6n+aHJ9U\nqc7SumRFUtce9cv98+an/YFFvjbtWG3Mcn+9yGWb/P8J9I/w1fVkCgECUyxQ/6grBAgQaLPA\nhl7l+q9trqu6TZ9AdXiqY/TRpG5HXz88rk3qKFK/c1RHkz6bPDT5QKK0U+CgVOup7ayaWrVE\noL8f6r+2pFqqsdwCOkjLLWp5BAgst8BvssA7JN9f7gVbHoFlEnhUlvPq5MHJfH84PTHj/zx5\ncfJviUKAQDcF7I+6ud0WXWsdpEWTmYEAgTEIfHcM67RKAk0FrsmE/yfZfzMzvCXjb5zUq0KA\nQLcF7I+6vf0a1d41SI2YTESAAAECBBYUqOuO5it1Gp5CgAABAh0RcASpIxtKNQkQIECAAAEC\nBAgQGL6AI0jDN7YGAgQIECBAYDCBuhNg3UZ9FKX/o/Go/jaqm3hcO4qGWQcBAosTGNU/Aour\nlakJECBwfYF7Z/DUpH9XsOt/aogAgUkVqJta1EN3R1nWj3Bldev3L41wfVa1dAH7o6Ubtn4J\nOkit30QqSGDqBXaOwMnJAclZU68BgMB0CTw7zR3VzS3qSNVNkl+NiLieWfWNEa3LapZHwP5o\neRxbvxQdpNZvIhUkMPUC/dNr+qe/TD0IAAJTJPDztLWiEGiDgP1RG7bCCOrgD44RIFsFAQIE\nCBAgQIAAAQLdENBB6sZ2UksCBAgQIECAAAECBEYgoIM0AmSrIECAAAECBAgQIECgGwI6SN3Y\nTmpJgAABAgQIECBAgMAIBHSQRoBsFQQIECBAgAABAgQIdENAB6kb20ktCUyzwNo0/rXJedOM\noO0ECBAgMHYB+6Oxb4LRVMBtvkfjbC0ECAwuUM8KecXgs5uTAAECBAgsi4D90bIwtn8hjiC1\nfxupIQECBAgQIECAAAECIxLQQRoRtNUQIECAAAECBAgQINB+AR2k9m8jNSRAgAABAgQIECBA\nYEQCOkgjgrYaAgQIECBAgAABAgTaL6CD1P5tpIYEpl1g+wD8Itlz2iG0nwABAgTGKmB/NFb+\n0a1cB2l01tZEgMBgArVDunmyy2Czm4sAAQIECCyLgP3RsjC2fyE6SO3fRmpIgAABAgQIECBA\ngMCIBHSQRgRtNQQIECBAgAABAgQItF9AB6n920gNCRAgQIAAAQIECBAYkYAO0oigrYYAAQIE\nCBAgQIAAgfYL6CC1fxupIQECBAgQIECAAAECIxLQQRoRtNUQIDCwwFWZ85xkzcBLMCMBAgQI\nEFi6gP3R0g07sYStO1FLlSRAYJoFrknj95tmAG0nQIAAgVYI2B+1YjMMvxKTegTpnqF73PD5\nrIEAAQIECBAgQIAAgUkSmNQO0oOzkV4ySRtKWwgQIECAAAECBAgQGL5AV0+xOzA0x2yB56b5\nbMfku71pPp/XF/beeyFAgAABAgQIECBAgMC8Al3tIF2c1tTRr9snpyY/TmaXgzKwKjmjN/L8\n3utiX/4kM+zVcKauWjZsnskIECBAgAABAgQITL5AV/+ovyCb5q7Jm5MnJ0cl70/65bV587Dk\nif0RA76+KPMdMOC8ZiNAYHkEVmYxb03+v+Ty5VmkpRAgQIAAgUUL2B8tmswM4xL4i6z4V8ln\nk5v1KlEdpDN770fxUn+0PWQUK7IOAlMosGvavCmpU2uVxQvUDz2nL342c3RUYGPq/eqO1l21\nCbRdwP5oaVuoM/ujSbhJwwnZVvWH02+TuuboUYlCgAABAgQIECBAgACBRQt09RS7uQ29JCMe\nkTwt+XByRXJRohAgQIAAAQIECBAgQKCxwCQcQZrd2CMyUDdo+EbyvdkfeE+AAAECBAgQIECA\nAIGFBCblCNLsdtYd7VwPNFvEewIECBAYtsD2WcFdkvrh8ZvJlcnccnBG1OngJ839wDABAgQI\ntEdg0o4gtUdWTQgQIEBgWgTqOtg6a+FryVeS85NDkrnlpRnxnLkjDRMgQIBAuwR0kNq1PdSG\nQFcEnpmKrk82jCD96wnruWajWF+to+6EqRBoIrAiEx2V1P8PhyeHJWclH0v+LlEIEBiugP3R\ncH2ncumTeIrdVG5IjSYwYoF/zfp+OMJ17p11/WSE63MN4wixO76qW6X+d0wekHwxqXJM8rrk\njcma5IhkqeXlWcBtGi5kh4bTmYzAJAjYH03CVmxZG3SQWrZBVIdARwTWpp7/0ZG6qiaBYQrs\nkYXXs4dOnbOS6tDsmNRDzH+WfD5ZSrk2M1cWKlWX1QtN5HMCEyRgfzRBG7MtTdFBasuWUA8C\nBAgQ6KLA+al0na7+sORfktnleRm4eXJs8mezPxjg/d83nOfRme68htOajAABAgTmEXAN0jwo\nRhEgQIAAgYYCv8p0n0nenbwnuUXSL3U0p65JqqNLdfOG/ROFAAECBFouoIPU8g2kegQIECDQ\neoEnp4ZfTepi8X3n1HZdhh+ZfDyp0/EUAgQIEGi5gFPsWr6BVI8AAQIEWi9wSWr48GSXpJ5z\nNLdclRHViarrkXaf+6FhAgQIEGiXgA5Su7aH2hAgQIBAdwXqYvEtla9v6UOfESBAgEA7BHSQ\nlmc7rMhibpdcvDyLm6qlrExr75ks9IfFVKFo7H8T2DljTvlvY41oIrBXk4lMMzEC9keDb0r7\no8HtpmlO+6PBt3Zn9kf1D6mydIHVWURnNvrSm2sJBAh0TKBuMf0XHauz6g4mYH80mJu5CBAY\njUAn9kc6SMvzZahfnVYtz6Kmbin/Iy1+W3LbqWu5BjcVuFEmrIfE3jU5q+lMprueQN0ooO6o\npky+gP3R4NvY/mhwu2mZ0/5o6Vu6E/sjp9gtfUPXEpo+wG951jZZS1nfa85lk9UsrVlGgfqD\nr0r9o3rNde/8hwCBzQnYH21OZuHx9kcLG037FPZHU/INcJvvKdnQmkmAAAECBAgQIECAwMIC\nOkgLG5mCAAECBAgQIECAAIEpEdBBmpINrZkECBAgQIAAAQIECCwsoIO0sJEpCBAgQIAAAQIE\nCBCYEgEdpCnZ0JpJgAABAgQIECBAgMDCAjpICxuZggABAgQIECBAgACBKRHQQZqSDa2ZBAgQ\nIECAAAECBAgsLKCDtLCRKYYrcFEW/4vhrsLSOy5wdep/QbK24+1QfQIE2i1gf9Tu7dOG2tkf\ntWErqAMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMA8Aj/K\nuNVbyA3mmWdLow7Kh8clP05+kByV3CpRuivwjlR9S9+RRy+yaXfM9J/qLfMreX1hsiJRCBCY\nbgH7o+ne/k1ab3/URGkKp9l6CtusycMV2DOLPzv57GZWs34z4+cbvX9GnpKcm7w3qc7VU5PT\nkwOTCxKlewK7pco3Sd66marX9m5a6jvxueTnyWuT2ySv6L3+dV4VAgSmV8D+aHq3fdOW2x81\nlTIdAQJLErgmc//jkpbw+5nraFEtb5ffj5q5Q95vSl49a5y33RKo7frrZaryy7Kcq5LdZy3v\nDXlfHfGdZ43zlgCB6ROwP5q+bb7YFtsfLVZsSqZ3BGlKNnRLm3lA6vWsZL/kZ8kxSR0N6Jcz\n8+a7ydr+iLx+L7k0qV8GlckX2CFNrFPm7pFsSL6a1CkR/SOR/573/5VcnPTLZXlT/7ZVB6ne\nKwQIEFhIwP5oISGf2x/5DhAgMLBA01/sHpw11LR1XVGdEnVEcm3yd8mWygPzYR1B+tstTeSz\nVgs0/cVu17Sirj2rTs77kjpqWKdVnprM9+POyoy/T3Jh8p+JQoDAdAvYH0339m/SevujJkqm\nIUBgyQK1Q6pf878xT/btLX3bvJ6f1PVFs8vLM1Dz33L2yFnv64hAHVWqC28Xe7OHWYvxdswC\ntUPakMz3Hfmfs+r27t50s78Pt8u4+TrIW2X8mt5nq/Pq9LogKASmXMD+aMq/AA2ab3/UAGka\nJ5nvV9hpdNDm5RW4JIv71jyLvLo3rq4jqj9668YLe/fG1cvJSXWe/iT552R2qWtM6nSqOrXu\nz5K67kTprkAdLZzvO/LLWU16aN6flFTnp/89WZf3ZycPTKoD1S/1b9lhyW2TZyZfT+ooZR2B\nUggQmF4B+6Pp3fZNW25/1FTKdAQIDCzQ5JSGx2TpdRRgc3nNnLXfOsN11OgXyR3nfGawewJN\nTmlYlWbVTmtz35Hvb6HZdSe7mm/u92gLs/iIAIEJFLA/msCNusxNsj9aZtBJWZwjSJOyJbvV\njv5NFw5Ntb84T9Vrp9Yv++dNTVOnTx2c/DRRJl9gfZp4ZVJHDZ89T3M39sbdKa81bd28o19+\nlDfVgfrj/givBAgQ2IyA/dFmYIz+nYD90e8ovCFAYFCBJr/Y7ZmF19GBo+es5G4ZPj6pP3qr\n3Cqp0yO+kuyUKJMh0OQXu2ppnXJZN2jYvgZ6pU7BPDb5m97w6Xk9L5n9Y88+Ga4O1BGJQoDA\n9ArYH03vtm/acvujplKmI0BgSQJNdki1gnpWUl2T9NrkVsl9k7q2pG7ZvFVS5dNJXcz/kuSv\n56SuQVG6KdB0h/TnaV6dKvfJ5D7JvsmRSX3H/jCp8sSkpnlXcqukvhdfTeoatbp9vEKAwPQK\n2B9N77Zv2nL7o6ZSpiNAYEkCTXdIdVTgbUlNX3/g1mkOdWTgdkmVGyc1fnOpP5qVbgo03SFV\n6x6d1I0b6ntQneWvJXVq5uzy3Az8Jul/V6qjXR0qhQCB6RawP5ru7d+k9fZHTZRMQ4DAyAVW\nZo23TuqifIXA5gRulg923dyHGV+n2FXn+qZbmMZHBAgQ2JKA/dGWdHzWF7A/6kt4JUCAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAi0U2BFO6ulVgQ6J/Cj1HibLdR6v3x21RY+n/3RMzLwv5N7J7+Y/YH3BAgQ\nIEBgAQH7owWAfExgIYGtF5rA5wQINBLYM1OdnXx2M1Ov38z4+UbvmJF7JSvn+9A4AgQIECCw\nBQH7oy3g+IhAEwEdpCZKpiHQTOD0TPbyZpOaigABAgQIDE3A/mhotBY8DQJbTUMjtZFAywQe\nl/r8a3Ji8q6kjhYtVOr/1acln0j68+09a6bb5P0Hk92S/y/5fPLx5OGJQoAAAQIE5hOwP5pP\nxbipF9BBmvqvAIARC/xj1ndUcqPkhOTOyZnJnZItlersvDepa5Kq81PXJ9V8f5RU2SOpDlQt\n89DkO0l1vP4teWSiECBAgACB2QL2R7M1vCdAgACBZRe4Jku8OPnGPNm3t7b75nVT8rzecL3U\ndUbfSr5aA73ygrzWdP0jS4/uDc8+GrQq485NvpfUzVb+OKl5PpL0y3Z5Ux2q6lwpBAgQIDAd\nAvZH07GdtXKIAq5BGiKuRU+dwCVpcXV25pareyMekte6WcOXk9mnx/1Hhl+Y3DC5Mplb/iwj\nViefnPVBLefTyf9K9pw1vo4Y9UvtJM9Jdu2P8EqAAAECUyFgfzQVm1kjhyWggzQsWcudRoE6\nCvTXW2j4rfNZHfmZrxNVs1WnqY4IzS23yYjz547M8BeS6iDdctZnF8x6X2/r1uI7zRlnkAAB\nAgQmW8D+aLK3r9YNWUAHacjAFk9glsDavK/8YbJu1vj+29/038x5vTTDt5szrga37407L6/7\n9N7XaXYKAQIECBDYkoD90ZZ0fDb1AltNvQAAAqMT+G5WtUtyz+TXs/K0vH9Hsrn/H2u+6lTN\nPpUugzMPSOq6p7rOSCFAgAABAk0F7I+aSpluKgU29wfZVGJoNIEhC9RtuH+evCU5PNkjeUry\n98lZSV1XNF95d0bW0aWPJQcmdQe8ZyVPTf5PohAgQIAAgcUI2B8tRsu0BAgQIDCQQN0QoW6Z\nulCp64m+kmxM6nS4HyfVyam72fXLC/KmPuvfxa7GH5CcltT4yvlJ3dihX/p3saujU7PLv2eg\n1qcQIECAwHQI2B9Nx3bWSgIECEycwA5p0S0HaFWdoje74zTAIsxCgAABAgR+J2B/9DsKbwgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAs1u0ZAAA7\njElEQVSAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCZeYMXEt3A0DfxOVnPr0azK\nWggQILBogc9mjkMWPZcZuihgf9TFrabOBKZHoBP7o62nZ3sMtaXVOXprcvpQ12LhBAgQWLzA\nIzPLgYufzRwdFbA/6uiGU20CUyDQmf2RDtLyfBs3ZTHVOfrM8izOUggQILBsAvtlSTpIy8bZ\n+gXZH7V+E6kggakV6Mz+aKup3UQaToAAAQIECBAgQIAAgTkCOkhzQAwSIECAAAECBAgQIDC9\nAjpI07vttZwAAQIECBAgQIAAgTkCOkhzQAwSIECAAAECBAgQIDC9AjpI07vttZwAAQIEli6w\nbRbxsuTOS1+UJRAgQIBAGwTcxa4NW0EdxilQzwLbP1m5DJW4WZax4zIsZzkXsSYLqyy1XJ0F\n/HCpCzE/gQkU2C5ten3yql7emNeNiUJgsQL2R83E7I+aOZlqCQI6SEvAM+tECDwgrThhIloy\n/EbcKqtYPfzVWAOBTgqcnFq/OnlQ8rTkB4lCYDEC9kfNtW6VSe2PmnuZcpECOkiLBDP5xAls\nt+22264//vjjVy21Zb/97W9n1q9fv9TFLOv8advMqlVLa9qaNWtmDj300KpXnUqkECAwv8A/\nZPTzk39Ovp98IXlvcnyyHEeUvpblHJQsVG6YCe6XeC7fQlLt+9z+aIFtYn+0AJCPl01AB2nZ\nKC2oqwIrVqyY2XHHpZ8ZtxzLaKNhdfwUAgQaCZyRqe6avCh5ZvLp5PzkqKQ++15ybjJIh+k5\nme8WyUKl1nnNQhP5vJ0C9kdb3i72R1v28enyCeggLZ+lJREgQIAAgXUhqGuS6lqkOt3u6Und\nxKF/nePH8v4xyWJLdbAqTcogHbAmyzUNAQIEpkJAB2kqNrNGEiBAgMCIBa7N+uo0t8oNkv16\ncXQnEAoBAgTaLKCD1Oato24ECBAgMAkCV6UR3+xlEtqjDQQIEJhoAR2kid68GkeAAAECQxa4\nIsu/R/LjIa/H4gkQIEBgRAI6SCOCthoCBAgQmEiBOpXu6xPZMo0iQIDAlAp0vYO0fbbbXZKt\nkjp94cpkbjk4I+o2XCfN/cAwAQIECBAgQIAAAQIEZgt0uYN0YBryyWSfXoMuyeuzkmN7w/2X\nl+bNpYkOUl/E6/UErr322q3e8573XG+cgd8LXHVVXT6hECBAgAABAgSmQ6CrHaQV2Tz1XIl6\nKufhvddn5LVun7p38qZEIdBIIB2kFUcdVV8nhQABAgQIECBAYNoFutpBulU23B2TByRfTKoc\nk7wuqWdPrEmOSBQCBAgQIECAAAECBAg0FuhqB2mPtLAehHfqnJa+PMM7Ju9PfpZ8PlEIECBA\ngAABAgQIECDQSKCrHaTz07q6McPDkn9JZpfnZeDmSV2L9GezP/CewHwCW2+99bXvf//76/uk\nzCOwdu3amRe84AXzfGIUAQIECBAgQGDyBLraQfpVNkU9nfzdyb2Sv09+kVSpI0uHJZ9OvpJc\nnnwtUQjMK7DVVlvNHHDAAfN+ZuTMzCWX1P1PFAIECBAgMFEC90lr9l+GFt0oy2jbHxGbUqe6\nu/PVy9C+OlvrzGVYTqcW0dUOUiE/OanrjJ6ZfDzpd5DydmZd8sjkvcnhyaDlSZmxf5e8LS1j\nu3x40y1N4DMCBAgQIECAAIF2CGyzzTbvTfbbYYcdNiylRtdcc83Kyy+/vHV/T++yyy6PXrVq\nVR00GLhcdtllq377299+YuPGjYcOvJCOzti6DboIx/pZ++HJLkk952huqXsTVyeqrkfafe6H\nDYfvnun2azDtykxzhwbTmYQAAQIECBAgQGDMAitWrFj5lKc8ZdVhhx22asxVGdbqt13qgt/0\npjfNfOpTn6q/caeudLmD1N9Ya/tvNvO6lCec13OVmpTqoS9UjybLMQ0BAgQIECBAgAABAmMU\ncGH6GPGtmgABAgQIECBAgACBdgl09QjSDmG89SIo6+jO6kVMb1ICBAgQIECAwEgFNmzYsNWL\nX/zika6zSyvL9TBdqq66dligqx2kut7n5EW4100cDlnE9CYlQIAAAQIECIxUIBfDr/jKV+oG\nvAoBAuMU6GoH6ZSgPSX5h+SryVuSLZULtvShz6ZboH6x+8xn6q7xSys//OEPZ666qu4N0p6y\n6667zuy1115LqlDuzrOk+c1MgAABAgQIEOiSQFc7SGV8ZLIiqVt9vyE5MVEILFZgde5k84s3\nv/nNS76Lzbp163bbtGlTq67ryzOe1uc2n0u+gUiWccX69evXLBbX9AQIECBAgACBrgl0uYNU\n1h9KHpO8MblHohBYrMC384f/LRc7U1emz+kaM87Z7srWUk8CBKZdID9GXXv88ce36oe2Nm2T\nNWvWzBx66NQ9kqdNm2Bq6tL1DlJtqLq2aO+k2rKkh31lfoUAAQIECBAgMBaBnNEws+OOO45l\n3V1YqR/8urCVJqOOk9BBqtOHzpiMzaEVBAgQIECAAAECBAiMU8Bh3HHqWzcBAgQIECBAgAAB\nAq0S0EFq1eZQGQIECBAgQIAAAQIEximggzROfesmQIAAAQIECBAgQKBVAjpIrdocKkOAAAEC\nBAgQIECAwDgFdJDGqW/dBAgQIECAAAECBAi0SkAHqVWbQ2UIECBAgAABAgQIEBingA7SOPWt\nmwABAgQIECBAgACBVgnoILVqc6gMAQIECBAgQIAAAQLjFNBBGqe+dRMgQIAAAQIECBAg0CoB\nHaRWbQ6VIUCAAAECBAgQIEBgnAI6SOPUt24CBAgQIECAAAECBFoloIPUqs2hMgQIECBAgAAB\nAgQIjFNAB2mc+tZNgAABAgQIECBAgECrBHSQWrU5VIYAAQIECBAgQIAAgXEKbD3OlVs3AQIE\nCBAgQIAAgTEIrDz77LNnPvOZz4xh1d1Y5erVq7tR0SHUUgdpCKgWSYAAAQIECBBYrMC6detW\nvuAFL1i/2PnmTv+jH/1o5fr1S17M3MUuaXiHHXbYtNdee21cykLiU7OvWsoy+vNu2rRp1Ze+\n9KWZirJ5gZUrV07l2WY6SJv/TviEAAECBAgsRmDXTHzprBnuk/cHJr9MTk4uSRQCmxM4bePG\nje846aSTVm5ugkWMv2umvcEiph/6pJdeeuklP/3pT89ehhVdlWVM76GNZQC0iIUFdJAWNjIF\nAQIECBDYksBt8+Hrkxsnf5rslHwiOTjpl01587zknf0RXgnMEbggwy+YM84gAQJjENBBGgO6\nVRIgQIDARAm8I63ZK3lJr1Vvz+u9k1cmxyY7J4ckNf7i5JhksaWOBuyw2JlMT4DA/AIrVqxY\nf//733/mXve61/wTGDtz/PHHz3z7299e0mmRXWXUQerqllNvAgQIEGiDwG6pxP2TOnJ0Uq9C\nj8jr0clresP1clqyb1KfDdJBqvkPSJqUmzeZyDQEplzg2v3222/mwQ9+8JQzbL753/ve96qD\ntPkJJvgTHaQJ3riaRoAAAQJDF7hF1lAXMZ/ZW9OKvNYvrl/rDc9+qavBD589YhHv/zzT1jVO\nC5WzMkFd86QQIECAwIACOkgDwpmNAAECBAhEoC46vzp5UfKKpK41+lxyeHJU0i/b581jk0F/\njr0w81YUAgQIEBiygA7SkIEtngABAgQmWmBDWvfs5Mjkzsk/JnVN0r8mX0+OTrZJDkv2Tp6Q\nKAQIECDQYgEdpBZvHFUjQIAAgU4IfDi1rKM7L00+lcwud+sNfCGvT0/Onf2h9wQIECDQPgEd\npPZtEzUiQIAAge4J1Gl1lT2SP0jq2qQ6cvTzZHXiuqAgKAQIEOiCgA5SF7aSOhIgQIBAVwTq\nWTaVb3SlwupJgAABAtcXqDvvKAQIECBAgAABAgQIECAQAR0kXwMCBAgQIECAAAECBAj0BHSQ\nfBUIECBAgAABAgQIECDQE9BB8lUgQIAAAQIECBAgQIBAT0AHyVeBAAECBAgQIECAAAECPYFJ\nuIvddmnLHZObJTdN6inmv07OTH7YG86LQoAAAQIECBAgQIAAgS0LdLmDVHV/bfKMZNfNNPP0\njH9q8t3NfG40AQIECBAgQIAAAQIEfifQ5VPsPpBW/E1yRHK/5A+TmyR7JnVE6ZDk4uSbyT0S\nhQABAgQIECBAgAABAlsU6OoRpJ3Tqiclf5l8fp4W/jzj6hS7jyfHJo9NTksUAgQIECBAgAAB\nAgQIbFagq0eQ9k6L6lqj/9hsy37/wRfz9r6/H/SOAAECBAgQIECAAAEC8wt0tYNUR4cuSR4+\nf7N+N7aOkNWpdj/43RhvCBAgQIAAAQIECBAgsBmBrp5itzHteV9yTHJY8unkgmRNsk1SN224\nXfL4ZN/kXolCgAABAgQIECBAgACBLQp0tYNUjXpN8vXk3cl8R5I2ZHxdg/TEpI44DVIOzUx1\nOl+TUrcbVwgQIECAAAECBAgQ6LBAlztIxX5Ccpuk7lx3y2Sn5PLkl71cndellAdk5ts3XMAN\nGk5nMgIECBAgQIAAAQIEWirQ9Q5Sn/VneVNZ7lLPUGpS6pS/S5tMaBoCBAgQIECAAAECBNor\n0NWbNLRXVM0IECBAgAABAgQIEOisQFePIO0Q8VsvQn1tpl29iOlNSoAAAQIECBAgQIDAFAp0\ntYN0h2yrkxexvepmDXW7b4UAAQIECBAgQGDKBTZt2nTthz70ofXHHnts3dRr4HLNNdesvPzy\ny1v39/Quu+yyftWqVXUJyMDlsssuW1VOAy+gwzO2boM2tDwl0z0l+Yfkq8lbki2VugW4QoAA\nAQIECBAgQGBm3bp1z0r2v+KKK5aqcaMsYP9kxVIXtIzzb1qzZs03s7yl3qysqnTqMtarM4vq\nagepgI9M6st4RPKG5MREIUCAAAECBAgQILCQwEmZoKIQ+G8CXb9Jw4fSoi8lb/xvLTOCAAEC\nBAgQIECAAAECixTo8hGkflPr2qJ6mGu1ZUnnkfYX6JUAgaEJ1LPF6hrCpZbdsoADlrqQZZ5/\nU5b3jeSqZVhunTpcD8JWCBAgQIAAgRELTEIHqe5Qd8aI3ayOAIEBBLbZZpv/s8MOO/zhjW50\noyX9mHHllVdudemll64aoApDm2XFihWbdt999/tvu+221VEauFx00UWr0r5/27hxoxvLDKxo\nRgIECBAgMLjAJHSQBm+9OQkQGKlAOhFbPf7xj1912GGHtapzs4wIK5e6rDe96U0zn/rUp7p+\n+vNSGcxPgAABAgTGJmAnPDZ6KyZAgAABAgQIECBAoG0COkht2yLqQ4AAAQIECBAgQIDA2AR0\nkMZGb8UECBAgQIAAAQIECLRNQAepbVtEfQgQIECAAAECBAgQGJuADtLY6K2YAAECBAgQIECA\nAIG2CeggtW2LqA8BAgQIECBAgAABAmMT0EEaG70VEyBAgAABAgQIECDQNgEdpLZtEfUhQIAA\nAQIECBAgQGBsAjpIY6O3YgIECBAgQIAAAQIE2iagg9S2LaI+BAgQIECAAAECBAiMTWDrsa3Z\nigkQIECAwGQI1I+NG+c05QYZflRyu+TC5ITkh4lCgAABAi0X0EFq+QZSPQIECBBotcCOqd1v\nksckH+vVtDpFn0v27g3Xy4bkFcnf14BCgAABAu0V0EFq77ZRMwIECBDopsBHUu06gvTc5Njk\n1skTkjckZyefShZb9skMN2k404qG05mMAAECBOYR0EGaB8UoAgSGJrDVRRddNHPOOecMbQVd\nX/DatWu73oRpr/8eAbh78r+Td/UwLsjrycmByWHJIB2kT2S+OyYLlU2ZoOqgECBAgMCAAovt\nINUvYjdM5v469duMu2zAOpiNAIEpEdi0adM2H/3oR2cqyuYFVq5c6QY6m+dp+yfVQanrkapD\nM7fUKXhPnzuy4fCdM92qBtNenWl+1WA6kxAgQIDAZgSa7oTrV6tvJ1cmFyV1wensfDDDCgEC\nBAgQmFaBut6ofkCsfeNJyR2SueWBGfGzuSMbDlenq36MXCgNF2cyAgQIENicQNMjSEdlAXXI\nvi4w/XlybTK7rJ494D0BAgQIEJgSgTpitD6pmy+8PqnzR+tIz7uTryTVYbpLUtcfPSA5JFEI\nECBAoMUCTTpIu6T+dd70w5NBzptucfNVjQCBUQqsWLFiw0EHHTSz3377jXK1nVrX6aefPvOT\nn/yk/uhWuiFwRaq5Q7J/clByp97rTfO6XVKl9p8HJ/Uj48cThQABAgRaLNCkg3RV6n9NcnmL\n26FqBAh0Q2DDfe9735nDDqvr1JX5BN70pjfpIM0H0+5x61K9M3o5slfVula339H9QN6/Lfl1\n7zMvBAgQINBigSbXINU//J9MBr2wtMXNVzUCBAgQIDAUgX7nqBZe1x3pHA2F2UIJECCw/AJN\njiDVWk9K3pR8q5e5d6z7TsZ/JFEIECBAgAABAgQIECDQWYGmHaTnpoV1it3NkgfP09p6krgO\n0jwwRhEgQIAAAQIECBAg0B2Bph2k23anSWpKgAABAgQIECBAgACBwQSadpD6S6878vTvylPX\nL9X8N05ukXwxUQgQIECAAAECBAgQINBZgSY3aajG3Sv5UVJP6K4LTStrknq+w1mJGzgEQSFA\ngAABAgQIECBAoNsCTY8g/WOauW3yuuSRyZlJdZjqeqS6/kgHKQgKAQIECBAgQIAAAQLdFmjS\nQapT6g5InpgcndQTwm+U1APv3picmFRH6ZhEIUCAAAECyylQzxOqH+ialmsz4fqmE5uOAAEC\nBAjMFWhyit1Omal2UKf2Zj47r3/Ue18Pkf2X5NDesBcCBAgQILCcAnfPwur07qapfZJCgAAB\nAgQGFmhyBOmiLP03yYHJuck5ye2TOopU1yJdk9w8UQgQIECAwHILnJ8FPnvWQvfK+xcln05O\nSC5O9kgel9w6eXeiECBAgACBgQWadJBq4Z9M6jqkOr3uM0k9KLZOr/t48qzks4lCgAABAgSW\nW6BuBvTeWQs9Je9flbxm1rh6+/6kfsB7aPK1RCFAgAABAgMJNDnFrhb8v5JvJAcndZrDS5Kn\nJ19MbpK4/igICgECBAgMVWDnLP2eyUfnWcvGjKtO0v3m+cwoAgQIECDQWKDpEaQ6la5uxLBN\nb8l1NOmryb7JCYkLYoOgECBAgMBQBS7P0usRE4ckr5tnTXfMuDqKpBAgQIAAgYEFmnaQ+iu4\nW94clFyT/FNyw0TnKAgKAQLNBL797W/PrFy5stnEm5nqsssumzn33Loksj1lxYoVM/vtt9/M\ndtvVjT8HLz/+8Y8Hn3ny56yjRJ9IXpjUD3f/maxN/iB5WvKE5EGJQoAAAQIEBhZo2kGqZx0d\nm/xFb03/kdc6xeG0pE5pqJ1VdZoUAgQIbFZgw4YN/3naaadtnWx2miYfXHvttTskuzWZdpTT\nnHLKKRemo7TkfwvTtrrORplfoK57XZe8Z87HdXTp4ckX5ow3SIAAAQIEFiXQtIP0zix1v+Sx\nSd3B7t7JVcnfJm9Jvpz836QNpU4DrHPQ90++mbhYNwgKgTYI5A//5yRtqMpQ6rBuXf3drgxZ\nYEOWX/ue1yZ1Sl0dPfpuUg8wtwGCoBAgQIDA0gSa3KShpnlM8tdJHTW6IqmyKXlfcmTykGTU\nZdessJ53UXfU+07y4KSOdH0/qV8Q357UdVJHJ/UcJ4UAAQIEJkPgRr1m1I2C6nbfD0xel9wh\nUQgQIECAwJIEmnSQbpw1bJ+s3syaavw4dkpHZb2PSD6ZVCepOkIfSeq89Icl9aviS5O6mLfu\nuKcQIECAQPcFHpQm/Cr5q15T/jWv1Tl6ZlJnDNTZDgoBAgQIEBhYYOsGc9aDYtckj09ePmf6\nOjLz1OSMOeOHPbhnVvCXSe0g/723sn/La51/Xqf/ndob98a81o0lHpl8oDduMS/V0dq74Qz9\nO/w1nNxkBAgQIDCAwDszTx05+mxSp1LfP6kfw96V1LjnJ34UC4JCgAABAoMJNOkg1ZLflrwy\nqSeY16l1N0xqB3R4cpukOkmjLHV0qO5mVHcw6pcv583ByWn9Eb3X2pE+a864poN19Kl2wE3K\nTk0mMg0BAgQIDCxQ+559kzrt+5dJ/XBX+6QPJ1cl9UPZYYlCgAABAgQGFmjaQaojMTsk9cvc\ntr213TOvdWTpKcnJvXGjejkvK6rTA2sn+U/JquTRSV2D9OdJXYPUL3+SN+f2Bxb52nRHW521\nSxa5bJMTIECAwOIEtsvkdeZC/04fdbrdmckFSZVbJD+97p3/ECBAgACBAQWadpCqA/Cy5O3J\nAcnNkrr2qHZMdWvVUZcLs8K63uiIpI5e1S+KVY+3Jh9M3pd8PakjQIcm47iJRFarECBAgMAy\nCtSPcj9JXpB8Nvnj5LVJlQck9aPWq2tAIUCAAAEC0yhQ1/y8KqnrjY5O7pjUuC8ndcpFZV3y\n4mTYpTqQdsrDVrZ8AgQGEXhRZjp9kBlbOs9fpF5XJvVv/BlJnTlQpzjXcJ09cJNkmov90TRv\nfW0n0G6BzuyPmh5B2jneD03qpgh7zGN/Ysa9cp7xwxxVnZ9X9TJ7PX+SgbpG6XbJdxKnvgVB\nIUCAwIQInJB21H7olslZSXWMqtw3+dp17/yHAAECBAgsQaBpB+ldWcfjkvoVsjoc/R1S3l5X\n6te8NpWfpzIVhQABAgQmT6BOqf7enGbpHM0BMUiAAAECgwk06SBtn0U/MnlGcuRgqzEXAQIE\nCBAgQIAAAQIE2i9Qd4JbqNTdgmq68xea0OcECBAgQIAAAQIECBDoskCTDlJd63NU8pzEs366\nvLXVnQABAgQIECBAgACBLQo0OcWuFvDc5Jzk4qTO8740mV3qTnJvnz3CewIECBAgQIAAAQIE\nCHRNoGkHqY4g3SqpZ1DUUaS5R5IGfRBrFqUQIECAAAECBAgQIECgHQJNOkh1k4aHJfWcn8rc\nO9hllEKAAAECBAgQIECAAIHuCzS5BqlaWZ2iz/Vea1ghQIAAAQIECBAgQIDAxAk06SBdnVYf\nnzw1WTFxAhpEgAABAgQIECBAgACBnkCTU+xq0hOTNyf3TL6bXJjMPtXuOxn+SKIQIECAAAEC\nBAgQIECgswJNO0jPSQsvS3ZP/mye1u6YcTpI88AYRYAAAQJTLVA/LO6THDPVChpPgACBDgk0\n7SDdtkNtUlUCBAgQINAWgQenInWjIx2ktmwR9SBAgMACAk07SAssxscECBAgQGAqBQ5Mq7fU\n+blpPq+zLOr09CqfT1543Tv/IUCAAIFWCuggtXKzqBQBAgQIdESgHqC+VXL7pB6a/uNkdjko\nA6uSM3ojz++9Lvblvplhz4YzrWw4nckIECBAYB4BHaR5UIwiQIAAAQINBS7IdHdN6kZGT07q\nwervT/rltXlTp9g9sT9iwNeXZr46WtWk7NZkItMQIECAwPwCOkjzuxhLgAABAgSaCtTjMP42\nqUdiHJk8NKlHY/wqWa7yoIYL2pjpLmo4rckIECBAYB6BOi1AIUCAAAECBJYucEIWUUd5fpvU\nNUePShQCBAgQ6JiAI0gd22CqS4AAAQKtFrgktXtE8rTkw8kViSM6QVAIECDQFQFHkLqypdST\nAAECBLokcEQqWzdo+EbyvS5VXF0JECAw7QKOIE37N0D7CRAgQGBYAnVHu4cMa+GWS4AAAQLD\nEXAEaTiulkqAAAECBAgQIECAQAcFdJA6uNFUmQABAgQIECBAgACB4QjoIA3H1VIJECBAgAAB\nAgQIEOiggA5SBzeaKhMgQIAAAQIECBAgMBwBHaThuFoqAQIECBAgQIAAAQIdFNBB6uBGU2UC\nBAgQIECAAAECBIYjoIM0HFdLJUCAAAECBAgQIECggwI6SB3caKpMgAABAgQIECBAgMBwBHSQ\nhuNqqQQIECBAgAABAgQIdFBAB6mDG02VCRAgQIAAAQIECBAYjsD/396dQElS13cAn1lYlkNR\nDgVDBDwxQdmoYGQBH67xiiJBEVCiQmJUXhLfM88DjWIkJioqqIkxHk9EEFS8IoISjBEQxYMn\nAYSERAUUuS9BOfbK98d2a9Nvdqdnpru3uvvzf+87dXTVv6o+1TP/+U9V1+ggDcZVrQQIECBA\ngAABAgQIjKCADtIInjS7TIAAAQIECBAgQIDAYAR0kAbjqlYCBAgQIECAAAECBEZQQAdpBE+a\nXSZAgAABAgQIECBAYDACOkiDcVUrAQIECBAgQIAAAQIjKKCDNIInzS4TIECAAAECBAgQIDAY\nAR2kwbiqlQABAgQIECBAgACBERTQQRrBk2aXCRAgQIAAAQIECBAYjIAO0mBc1UqAAAECBAgQ\nIECAwAgK6CCN4EmzywQIECBAgAABAgQIDEZg48FUO9Rat87Wbu7Y4t4Zf1zyi+S85MZEIUCA\nAAECBAgQIECAwKwCo9xBenSO7h+SbZOnJlsmX0ielrTLmoy8Jnl/e4YhAQIECBAgQIAAAQIE\n1iUwyh2k9+WgdkyObB3ccRkuS96afDZ5QHJQUvNvSE5OFAIECBAgQIAAAQIECKxTYFQ7SNvk\niJ6e1JWjb7WO7oAMT0qObk3X4LvJI5N6bT4dpGdlvZ2SXsriXhayDAECBAgQIECAAAECzRUY\n1Q7SDiGtB0xc1KKdznB1cm5runPw9Uwc1jljDuN/kWV37XH5umKlECBAgAABAgQIECAwwgKj\n2kG6NOZ3Jq9Ljkrqs0ZfTQ5LTkzaZbOMvCi5sD1jjsMX9Lh8dc769jCIxYsXH79o0aLlPW57\nnYutWrVq85UrVzau47bJJpvcND09fc86d7zHF+6+++4PZNH39ri4xQgQIECAAAECBAjMKjCq\nHaSVObK/So5PnpB8OKnPJJ2SfC85KdkkOTR5WPKSZGRKOkd77bPPPjvuvvvuC9rn2267beqn\nP/3pguro98rpGE095jGP2X7JkiULqvqMM86Yuuiiix6/oEqsTIAAAQIECBAgQKBLYFQ7SHUY\nn0iuS96Y/FvSWfZoTfx7hnWb3I87XxyF8aVLl04dcEB9dEqZSeDSSy+tDtJML5lHgAABAgQI\nECBAYN4Co9xBqoOu2+oq2ye/m9Rnk+rK0c+TK5NfJAoBAgQIECBAgAABAgR6Ehj1DlL7IK/N\nSOUH7RmGBAgQIECAAAECBAgQmKtAPQlOIUCAAAECBAgQIECAAIEI6CB5GxAgQIAAAQIECBAg\nQKAloIPkrUCAAAECBAgQIECAAIGWgA6StwIBAgQIECBAgAABAgRaAuPykAYnlAABAgQINE2g\nnqr61GTn5NvJxYlCgAABAg0X0EFq+AmyewQIECDQeIG6G+NNyf7JNcnbk58klyTbJVXWJMck\nR9aEQoAAAQLNFXCLXXPPjT0jQIAAgdEQOC67eXRye/Lk5LTkY0n9+4lDkiclH03ekLwgUQgQ\nIECgwQKuIDX45Ng1AgQIEGi8wGbZwyOSP01OTjZPvpTU1aS9krq1rsr3k0cnhyWfT+Zaqv6H\n97jSpj0uZzECBAgQmEFAB2kGFLMIECBAgECPAo/Lchsl1Smq8uukOkp7JO3OUUbvLV/I11e1\nxuc62Dsr/F6PK1WnTSFAgACBeQroIM0TzmoECBAgQCACdRtd3a5eD2M4PalSnaV7kumkPnvU\nLk/PyFXtiTkOe+1YrU69t8yxbosTIECAQIdA/VBXCBAgQIAAgfkJVIenOkafTt6X1B8eb03q\nKlK7c1RXk85I9ks+kigECBAg0GABHaQGnxy7RoAAAQIjIXBg9vJtyXOSlTPs8Usz74+S1ydf\nnOF1swgQIECgQQI6SA06GXaFAAECBEZS4K7s9XuSXdex9+/O/G2TGioECBAg0HABn0Fq+Amy\newQIECAwMgL1uaOZSt2GpxAgQIDAiAi4gjQiJ8puEiBAgAABAgQIECAweAFXkAZvbAsECBAg\nQGBUBepJfPUY836UftXTj32pOuohGvXUv36UmT571o961UGAwAYQ0EHaAOg2SYAAAQIERkFg\n4403/ubKlSufMgr7uoH38bhs/2828D7YPAECfRLQQeoTpGoIECBAgMC4CWy00UYPOfDAA6eW\nL1++oEO76667pq6++uqpNWvaTz5fUHV9WXl6enpq5513nsoxLqi+E044Yeq888578IIqsTIB\nAo0S0EFq1OmwMwQIECBAoFkCO+yww9TSpUubtVMN2pttttmmQXtjVwgQ6IeAhzT0Q1EdBAgQ\nIECAAAECBAiMhYAO0licRgdBgAABAgQIECBAgEA/BNxi1w/FPteRe7QXn3baaVMXXnhhn2se\nn+ouu+yy8TkYR0KAAAECBAgQINAYAR2kxpyK++zIossvv3yqohAgQIAAAQIECBAgMDwBt9gN\nz9qWCBAgQIAAAQIECBBouIAOUsNPkN0jQIAAAQIECBAgQGB4Am6xG551z1vK/2a454gjjpja\nf//9e15n0hY87rjjps4888xJO2zHS4AAAQIECBAgMGABHaQBA8+z+jWbb7751FZbbTXP1cd/\ntSVLloz/QTpCAgQIECBAgACBoQu4xW7o5DZIgAABAgQIECBAgEBTBXSQmnpm7BcBAgQIECBA\ngAABAkMX0EEaOrkNEiBAgAABAgQIECDQVAEdpKaeGftFgAABAgQIECBAgMDQBTykYejkNkiA\nAAECBEZGYNFVV101dcEFF4zMDg97R2+88cZhb9L2CBAYsIAO0oCBVU+AAAECBEZVYM2aNYtP\nPfXUqYpCgACBSRFwi92knGnHSYAAAQIECBAgQIDArAI6SLMSWYAAAQIECBAgQIAAgUkRcIvd\npJxpx0mAAAECBOYoMD09vWrPPfec2nXXXee45uQsfs4550xdfvnlk3PAjpTABAjoIE3ASXaI\nBAgQIEBgngIrly1bNnXQQQfNc/XxX+3666/XQRr/0+wIJ0zALXYTdsIdLgECBAgQIECAAAEC\n6xbQQVq3jVcIECBAgAABAgQIEJgwgXHtID055/HFE3YuHS4BAgQIECBAgAABAgsUGNcO0nPi\ncuQCbaxOgAABAgQIECBAgMCECYzqQxoel/N08nrO1XZ57f7Jxa1lzszwta1xAwIECBAgQIAA\nAQIECMwoMKodpBtyNHX16/eT7yT/l3SWP8jE4uSHrZlXtIZzHeybFXbscaVRtezx8CxGgAAB\nAgQIECBAYPwFRvWX+mtzanZPjkkOT05MPpS0y99nZP/kpe0Z8xy+Lus9dp7rWo0AAQIECBAg\nQIAAgRETGNUOUjHfmfx1cnpyfLJf8ufJNUm/Sn2WqZdyexaqK1kKAQIECBAgQIAAAQIjLDAO\nD2n4WvzrM0l3J/WZowMThQABAgQIECBAgAABAnMWGOUrSJ0He2MmDkhennwiuSO5PlEIECBA\ngAABAgQIECDQs8A4XEHqPNiPZaIe0PCD5JLOF4wTIECAAAECBAgQIEBgNoFxuYLUeZz1RLvn\nds4wToAAAQIEBiywWep/YlJ/eLwg+VXSXZ6WGXU7+Le6XzBNgAABAs0RGLcrSM2RtScECBAg\nMCkC9TnYumvh3OTs5IrkoKS7vDEzXt090zQBAgQINEtgHK8gNUt4HnuzevXqu4899tg1lXms\n/ptV1qxZM/2biQaNTE/fu1v9OLYVDTosu0KAwGQK1A+0E5P6eXRYa/iKDD+TPCx5VzKyRXs0\n+6lrtbXao9mpLEFgZAR0kBp4qlasWFEPnNipD7u2Rep4dB/q6WcVq9OY1F9aV/Wh0h/1oQ5V\nECBAYCECO2flpckzkrOSKicnb0/emdyU1OdjF1renAoe1WMl9+txuVkX0x7NStReQHvUljAk\nMAYCOkjNPIn1OaqKQoAAAQLNFtg+u7c66f5feNWhuX9S/8T8Z8mZyUJK/VGplz8s1b5cuZAN\nda2rPeoCMUmAwPgL6CCN/zl2hAQIECAwOIErUnV9nnf/5FNJZ3lNJn4n+WyyvPOFeYy/o8d1\nXpjlftLjshYjQIAAgRkEPKRhBhSzCBAgQIBAjwLXZLmvJP+U/HOyQ9IudTXn0KSuLp2d7Joo\nBAgQINBwAR2khp8gu0eAAAECjRc4PHt4TnJE8siuvb0n089PTk3qdjyFAAECBBou4Ba7hp8g\nu0eAAAECjRe4MXv4J8kDk/o/R93l15lRnaj6PNKDul80TYAAAQLNEtBBatb5sDcECBAgMLoC\nt86y69+b5XUvEyBAgEADBHSQ+nMSplPNLskN/amukbUsy17d1sg9G7+dqkf01v/UmOkv0eN3\ntBv+iOpx+OP8i+uOG57YHgxRQHs0ROwJ2JT2aLgnWXs0XO91bq1+kCoLF7gyVfglZOGOaiBA\nYDAC9YjpZw2marU2TEB71LATYncIELiPwEi0RzpI9zln857YKGsunvfazV9xj+xifQB5u8RV\njcGfr89kE/+THDX4TU38FnaLQL23H5zcPsYa9aCAeqKaMv4C2qPxP8fDPELt0fC0tUfDs551\nS26xm5WopwV6/Qd+PVXWwIXql6sqv0zuunfMl0EKrEzlZe6WxkEqr637jtYmquPvvT14b1sY\nvID2aPDGk7QF7dHwzrb2aHjWs27JY75nJbIAAQIECBAgQIAAAQKTIqCDNCln2nESIECAAAEC\nBAgQIDCrgA7SrEQWIECAAAECBAgQIEBgUgR0kCblTDtOAgQIECBAgAABAgRmFdBBmpXIAgQI\nECBAgAABAgQITIqADtKknGnHSYAAAQIECBAgQIDArAI6SLMSWYAAAQIECBAgQIAAgUkR0EGa\nlDO9sOO8Oav/IlmxsGqs3aPANVnu2h6XtdjCBG7J6uXtHyAvzNHaBIYloD0alvTa7WiPhuet\nPRqetS0RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIYs8KRs\n78rk8evZ7u6tZZ64nmW6X+ql3u51Jm36nTngsyftoEfoeHt532+T46nvn+eP0HHZVQJNFeil\n3ejl+7L7+Hqpt3udSZvWHjX7jPfyvtceDfgcLhpw/apvlsCS7M6OSQ3XVe7JC9cnK9a1wAzz\ne6l3htUmatbWOdqHTNQRj9bB9vK+r5+X9f2zxWgdmr0l0EiBXtqNXr4vuw+ul3q715m0ae1R\ns894L+977dGAz+HGA65f9aMncFF2eY/R2217TGBBAt73C+KzMoGBCPi+HAirShsu4H3fgBPk\nClIDTsIG2IVNs82jkrOSk5LnJe3yiIx8NKlhuzw8I29JavmPJI9MjkmekXSW9dXbuZzxtQJ1\nK8jxyTnJiclzk3Y5ICMfTKbbMzLcL6lz03klqm6XrHlbJspagfdk8JTkkORzyeeTg5Mq9Z49\nOTklKe/2z8CZ3vc1763J15MPJJ3umVQIEOiDwPrajZm+L7VHfUCfoQrt0QwofZilPeoDoioI\nDFpgn2xgTXJNUp+HqfuQL0lWJe1O0l4Zr2WWJVW2S36e3JTU8h9Pbkx+lbw2qdJLvWuXnNyv\n1bG8vOPwX5Tx1cnXktcl1VEt97cnVZ6Z1HTn58W+2pr3sgzb5diM/Hd7wvBegXp//zAp72qc\nLkjK+v3JDcm/JKcn5fvUpEr3+/7BmfezpN7370hOTK5Lap2XJAoBAgsT6KXd6P6+1B4tzLy9\ntvaoLTH4ofZo8Ma2QGDBAu0G6dTU1L4ysVnGf53UVYgq3Q1SLXtrcv96sVXenWH9otjdQVpf\nve11J3XY2SBtFYSbk091Ybw30yuT3ZJNkl8mRyZVliTVKa0ftick7VKdgOq4Kr8VKKPqzFQn\np0q9x+9O6j1bf5GuUu//6ljWlaEq3e/7UzLvqqTzff+uTOsgBUEh0AcB7VEfEOdZhfZonnDz\nWE17NA+0JqzSvr2kCftiH4Yn8Llsqn7Rq3Jncl5St83NVKoROyu5vePFL3aMd47Opd7O9SZt\nvK4KVSeprmh0ls9nYqPk6ck9SV1demZSpX6Br1JXP9pXPR6V8cqXEuW+At/JZD1spEq9xy9N\nLk5+nFSp9/9PkqU1MUPZM/M+nXS+7z80w3JmESCwMIG5tBvao4VZz7S29mgmlf7O0x7113Mo\ntekgDYW5cRupv4x3ljsyUVcsusu2mVG3NJzd9cL5ma6rTt2l13q715u06erUVLni3q+//VKu\nddVop9asL2e4LNkiqU7Tt5LqND00qTqem1yTfDdR7ivQ/V5ckZfrilFnqXkzlftlZp2Dn3e9\nWLfc1a16CgEC/RPo/l7VHvXPtpeatEe9KC1sme73uPZoYZ5DWXvRULZiI00TaF89mm2/bssC\nq5Ituxasqxybds2ryV7rnWHViZpVt9dVeeDawX2+1u1gdWWjyhlJWdcVo+og/UdyQVK3PC5P\nqoNUnSjuQegq6+r8dC32m1tNO+fXL2hlXFf5Oku95/3M7BQxTmDhAr3+/NIeLdx6phq0RzOp\n9Hee9qi/nkOpTWM/FOaR3Uh9U9fVied1HcF+mfbe6UKZw2Td6lWlnqjWWfbNxOKkHjBQpRqu\nc5ODk7oNop6mVlcwvpE8P6nbTdxeF4QBlOqIVie0s+zdOWGcAIGhCmiPBsOtPRqMaz9r1R71\nU7PHuvyS2yPUBC/2lhz77sm3k0OS9yXHJ8r8BepWr1OSo5Pq6GyZPCX5aFK30XXeMldXiF6c\n1BWNC5Mq1VGqzlV9tqY6S0r/Bf4xVdbnkOocPSh5afKviUKAwIYT0B7131571H/TfteoPeq3\naA/16SD1gDThi9Qv4M9O6qlf9eSbumpxaFJlps8hrX3F19kEXpEF6ql/n07q1pGvJPVXoj9O\n7krapTpI9X36n8nq1szqIFX5alIPc1D6L1Dv+8OT6pzWwx4+nLwzWZUoBAhsGAHt0WDctUeD\nce1Xrdqjfkmqh0AfBXZLXVt31bdTpuu+8Rd2zTc5d4G6pa6eILjx3Fe1xpAE6v2+yZC2ZTME\nCKxbQHu0bpt+vKI96ofiYOvQHg3WV+0EehY4LUv+b7J5a416aMCJSV092r41z4AAAQIECAxa\nQHs0aGH1EyBAgEBPAk/MUtcm9XmX85O6HaxuAXtGohAgQIAAgWEJaI+GJW07BCZcoD5XohCY\nTaDeJ/UUtb2SuppUDxG4JVEIECBAgMAwBbRHw9S2LQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIECBAgMCECB+c4r+zKf2X6rOTwZHGiECBAgACBQQtojwYtrP6xFth4rI/OwREYrsAW\n2dyOyceS65JFyXbJLsnHk1cmeycrE4UAAQIECAxKQHs0KFn1EiBAgMCcBP4sS69Jls6w1hta\nrx09w2tmESBAgACBfgpoj/qpqS4CBAgQmLfA+hqkqvTkZEWyWU20yrYZviOp2/C+nLw+WZJ0\nlidn4pPJuckpSd2u110emxkfSr6ZnJg8O1EIECBAYDIFtEeTed4ddZ8EFvWpHtUQIDC7wBez\nSN3Wuktr0e0z/FFyUFKdnxo/Mvlu0u5EPSHj30welHwluSv5SHJM0i7PycgPkuXJN5K7k1q2\nrlopBAgQIECgW0B71C1imgABAgQGIjDbX+yWZat1C96hra1/NsPrkq1a0zWoq0W1zN8lVY5O\nbk46/5jxtkzX55yq1NWmK5JvJ53lzZmoztROnTONEyBAgMBECGiPJuI0O8hBCXT+0jWobaiX\nAIG1AtWZqbLR2sG9V3w+mfFbWtM1OD+5OnlWTaRcklQH6tTkgGTL5K3Jy5MquyXVCaq/Bj6s\nI+dlvLa3b6IQIECAAIFOAe1Rp4ZxAgQIEBiYwGx/satOTV0d2iepTk+N/2XSXeqJd9d2zKxb\n5W5Kavl7kjOTxydVDklq/rpydC2kECBAgMBECWiPJup0O9h+C9TnIRQCBIYjsDybqc8H1WeN\nbk/qcd8PTLpLff7oxx0z35Xx9yZ7JnVlqR7SUFeIdkhuTaocnNSDHrpL3WanECBAgACBTgHt\nUaeGcQIECBAYmMD6/mK3b7a6OqkHLLTLRRk5uz3RGm6aYd1i98HW9KszPL413h7UrXZ1xWjf\n5KHJquSkpLPskYnTk/aVps7XjBMgQIDAeAtoj8b7/Do6AgQIjIxAu0H6++zxK5NXJUclpyR1\ntejSZLukXdodnbpCVPN3TurBDXVVqN2xeUrGqzP0pqQ6Q/WZo3oc+B3J5kmVDyd3JrXdnZNa\np7Z1frIoUQgQIEBgsgS0R5N1vh0tAQIEGivQbpA6Pw9UD2C4OHlbss0Me/6yzLshqXWqE/X9\n5A+TzvK3mairTe16L8v47h0L1C15xyZ1O10tUx2s6mjtkigECBAgMHkC2qPJO+eOmAABAmMn\nsGOO6AGzHNX2eX3r9SxTT8d7RLJ4Pct4iQABAgQIrE9Ae7Q+Ha8RIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQaJDA/wOQVGrrI5HNFgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,2))\n",
    "\n",
    "boxplot(`mean` ~ `FeIon`, data=grouped_means_sds)\n",
    "boxplot(`sd` ~ `FeIon`, data=grouped_means_sds)\n",
    "boxplot(`mean` ~ `Dose`, data=grouped_means_sds)\n",
    "boxplot(`sd` ~ `Dose`, data=grouped_means_sds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loaded-recognition",
   "metadata": {},
   "source": [
    "从比较结果来看，高剂量组标准差明显异于其他两组，认为假定误差的等方差性不太合理。\n",
    "所以不能直接进行方差分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extreme-delta",
   "metadata": {},
   "source": [
    "### (2)\n",
    "\n",
    "![题（2）](https://tva1.sinaimg.cn/large/008i3skNly1gr7gc6aor7j61mu04iq4i02.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sized-steel",
   "metadata": {},
   "source": [
    "自然对数变换，把变换后的数据列叫做 `lnRetention`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "crude-virtue",
   "metadata": {},
   "outputs": [],
   "source": [
    "lnRetention <- log(Retention)\n",
    "data <- cbind(data, lnRetention)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "physical-participation",
   "metadata": {},
   "source": [
    "计算变换后的分组均值、标准差："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "nutritional-format",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>FeIon</th><th scope=col>Dose</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Fe2</td><td>high</td><td>1.680129</td><td>0.4645464</td></tr>\n",
       "\t<tr><td>Fe3</td><td>high</td><td>1.160924</td><td>0.5854773</td></tr>\n",
       "\t<tr><td>Fe2</td><td>low </td><td>2.403389</td><td>0.5693701</td></tr>\n",
       "\t<tr><td>Fe3</td><td>low </td><td>2.279981</td><td>0.6563113</td></tr>\n",
       "\t<tr><td>Fe2</td><td>mid </td><td>2.090045</td><td>0.5736511</td></tr>\n",
       "\t<tr><td>Fe3</td><td>mid </td><td>1.901225</td><td>0.6585116</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " FeIon & Dose & mean & sd\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Fe2 & high & 1.680129 & 0.4645464\\\\\n",
       "\t Fe3 & high & 1.160924 & 0.5854773\\\\\n",
       "\t Fe2 & low  & 2.403389 & 0.5693701\\\\\n",
       "\t Fe3 & low  & 2.279981 & 0.6563113\\\\\n",
       "\t Fe2 & mid  & 2.090045 & 0.5736511\\\\\n",
       "\t Fe3 & mid  & 1.901225 & 0.6585116\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| FeIon &lt;chr&gt; | Dose &lt;chr&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| Fe2 | high | 1.680129 | 0.4645464 |\n",
       "| Fe3 | high | 1.160924 | 0.5854773 |\n",
       "| Fe2 | low  | 2.403389 | 0.5693701 |\n",
       "| Fe3 | low  | 2.279981 | 0.6563113 |\n",
       "| Fe2 | mid  | 2.090045 | 0.5736511 |\n",
       "| Fe3 | mid  | 1.901225 | 0.6585116 |\n",
       "\n"
      ],
      "text/plain": [
       "  FeIon Dose mean     sd       \n",
       "1 Fe2   high 1.680129 0.4645464\n",
       "2 Fe3   high 1.160924 0.5854773\n",
       "3 Fe2   low  2.403389 0.5693701\n",
       "4 Fe3   low  2.279981 0.6563113\n",
       "5 Fe2   mid  2.090045 0.5736511\n",
       "6 Fe3   mid  1.901225 0.6585116"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_ln_means <- aggregate(lnRetention, by=list(FeIon, Dose), FUN=mean)\n",
    "grouped_ln_sds   <- aggregate(lnRetention, by=list(FeIon, Dose), FUN=sd)\n",
    "\n",
    "# 下面几行代码将结果整合到一个表格，方便查看：\n",
    "grouped_ln_means_sds <- cbind(grouped_ln_means, grouped_ln_sds[\"x\"])\n",
    "names(grouped_ln_means_sds) <- c(\"FeIon\", \"Dose\", \"mean\", \"sd\")\n",
    "grouped_ln_means_sds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "industrial-dietary",
   "metadata": {},
   "source": [
    "作图比较："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "weighted-pregnancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1VSN2eo4TckJyQKAQIECDRUoK0dpF3i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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,2))\n",
    "\n",
    "boxplot(`mean` ~ `FeIon`, data=grouped_ln_means_sds)\n",
    "boxplot(`sd` ~ `FeIon`,   data=grouped_ln_means_sds)\n",
    "boxplot(`mean` ~ `Dose`,  data=grouped_ln_means_sds)\n",
    "boxplot(`sd` ~ `Dose`,    data=grouped_ln_means_sds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continent-agency",
   "metadata": {},
   "source": [
    "可以看到，现在个组标准差趋于一致，各族间标准差差异不大。\n",
    "可以利用变换之后的数据进行方差分析了。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quantitative-parker",
   "metadata": {},
   "source": [
    "### (3)\n",
    "\n",
    "![题（3）](https://tva1.sinaimg.cn/large/008i3skNly1gr7grannygj31le050q51.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "vietnamese-course",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             Df Sum Sq Mean Sq F value   Pr(>F)    \n",
       "FeIon         1   2.07   2.074   5.993   0.0161 *  \n",
       "Dose          2  15.59   7.794  22.524 7.91e-09 ***\n",
       "FeIon:Dose    2   0.81   0.405   1.171   0.3143    \n",
       "Residuals   102  35.30   0.346                     \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lnRetention.aov <- aov(lnRetention ~ FeIon + Dose + FeIon:Dose, data=data)\n",
    "summary(lnRetention.aov)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proprietary-privacy",
   "metadata": {},
   "source": [
    "从结果中可以看出，在显著水平 $\\alpha=0.05$ 下，\n",
    "铁离子种类因素（$\\textrm{Fe}^{2+}$、$\\textrm{Fe}^{3+}$）和\n",
    "剂量因素（剂量低、中、高）对存留量的影响均显著（检验 p 值都小于 $0.05$）。\n",
    "即说明两种铁离子存留量是有显著差异的，不同剂量水平下存留量也是有显著差异的。\n",
    "\n",
    "同时，可以看到在该水平下，交叉因子 (`FeIon:Dose`项）对存留量影响不显著（$p=0.3143>0.05$），认为两种铁离子存留量在不同剂量水平下可认为是相同的。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "temporal-vegetation",
   "metadata": {},
   "source": [
    "### (4)\n",
    "\n",
    "![题（4）](https://tva1.sinaimg.cn/large/008i3skNly1gr7haazrvej31li050acr.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "concrete-white",
   "metadata": {},
   "source": [
    "先求各因素在不同水平下的均值以及估计区间，可以复用 3.5 中封装的 mean_confin 函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "returning-suicide",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_confin <- function(x, ...) {    # mean and confidence interval of x by t.test\n",
    "    t.res <- t.test(x, ...)\n",
    "    mean_val <- t.res$estimate[[\"mean of x\"]]\n",
    "    mean_conf.in <- t.res$conf.in\n",
    "    \n",
    "    res <- c(mean_val, mean_conf.in)\n",
    "    names(res) <- c(\"mean\", \"conf.left\", \"conf.right\")\n",
    "    \n",
    "    res  # ret\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imported-david",
   "metadata": {},
   "source": [
    "按离子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "unlimited-constitution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mean</th><th scope=col>conf.left</th><th scope=col>conf.right</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Fe2</th><td>2.057854</td><td>1.892251</td><td>2.223458</td></tr>\n",
       "\t<tr><th scope=row>Fe3</th><td>1.780710</td><td>1.568011</td><td>1.993409</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 3 of type dbl\n",
       "\\begin{tabular}{r|lll}\n",
       "  & mean & conf.left & conf.right\\\\\n",
       "\\hline\n",
       "\tFe2 & 2.057854 & 1.892251 & 2.223458\\\\\n",
       "\tFe3 & 1.780710 & 1.568011 & 1.993409\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 3 of type dbl\n",
       "\n",
       "| <!--/--> | mean | conf.left | conf.right |\n",
       "|---|---|---|---|\n",
       "| Fe2 | 2.057854 | 1.892251 | 2.223458 |\n",
       "| Fe3 | 1.780710 | 1.568011 | 1.993409 |\n",
       "\n"
      ],
      "text/plain": [
       "    mean     conf.left conf.right\n",
       "Fe2 2.057854 1.892251  2.223458  \n",
       "Fe3 1.780710 1.568011  1.993409  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(function() {\n",
    "    Fe2 <- mean_confin(data[FeIon==\"Fe2\",]$lnRetention)\n",
    "    Fe3 <- mean_confin(data[FeIon==\"Fe3\",]$lnRetention)\n",
    "    rbind(Fe2, Fe3)\n",
    "})()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-rogers",
   "metadata": {},
   "source": [
    "按剂量："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "institutional-madagascar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 3 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mean</th><th scope=col>conf.left</th><th scope=col>conf.right</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Low</th><td>2.341685</td><td>2.135709</td><td>2.547661</td></tr>\n",
       "\t<tr><th scope=row>Mid</th><td>1.995635</td><td>1.787163</td><td>2.204107</td></tr>\n",
       "\t<tr><th scope=row>High</th><td>1.420526</td><td>1.223052</td><td>1.618001</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 3 × 3 of type dbl\n",
       "\\begin{tabular}{r|lll}\n",
       "  & mean & conf.left & conf.right\\\\\n",
       "\\hline\n",
       "\tLow & 2.341685 & 2.135709 & 2.547661\\\\\n",
       "\tMid & 1.995635 & 1.787163 & 2.204107\\\\\n",
       "\tHigh & 1.420526 & 1.223052 & 1.618001\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 3 × 3 of type dbl\n",
       "\n",
       "| <!--/--> | mean | conf.left | conf.right |\n",
       "|---|---|---|---|\n",
       "| Low | 2.341685 | 2.135709 | 2.547661 |\n",
       "| Mid | 1.995635 | 1.787163 | 2.204107 |\n",
       "| High | 1.420526 | 1.223052 | 1.618001 |\n",
       "\n"
      ],
      "text/plain": [
       "     mean     conf.left conf.right\n",
       "Low  2.341685 2.135709  2.547661  \n",
       "Mid  1.995635 1.787163  2.204107  \n",
       "High 1.420526 1.223052  1.618001  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(function() {\n",
    "    Low  <- mean_confin(data[Dose==\"low\",]$lnRetention)\n",
    "    Mid  <- mean_confin(data[Dose==\"mid\",]$lnRetention)\n",
    "    High <- mean_confin(data[Dose==\"high\",]$lnRetention)\n",
    "    rbind(Low, Mid, High)\n",
    "})()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-bread",
   "metadata": {},
   "source": [
    "利用 DescTools 包，求 Bonferroni 同时置信区间："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "answering-cleaners",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  Posthoc multiple comparisons of means : Bonferroni \n",
       "    95% family-wise confidence level\n",
       "\n",
       "$FeIon\n",
       "              diff     lwr.ci      upr.ci   pval    \n",
       "Fe3-Fe2 -0.2771441 -0.5016931 -0.05259515 0.0161 *  \n",
       "\n",
       "$Dose\n",
       "               diff     lwr.ci       upr.ci    pval    \n",
       "low-high  0.9211588  0.5836659  1.258651627 4.6e-09 ***\n",
       "mid-high  0.5751084  0.2376156  0.912601307 0.00021 ***\n",
       "mid-low  -0.3460503 -0.6835432 -0.008557451 0.04251 *  \n",
       "\n",
       "$`FeIon:Dose`\n",
       "                        diff      lwr.ci     upr.ci    pval    \n",
       "Fe3:high-Fe2:high -0.5192055 -1.10861287 0.07020194  0.1408    \n",
       "Fe2:low-Fe2:high   0.7232596  0.13385220 1.31266701  0.0055 ** \n",
       "Fe3:low-Fe2:high   0.5998524  0.01044505 1.18925985  0.0425 *  \n",
       "Fe2:mid-Fe2:high   0.4099156 -0.17949182 0.99932299  0.5859    \n",
       "Fe3:mid-Fe2:high   0.2210958 -0.36831157 0.81050323  1.0000    \n",
       "Fe2:low-Fe3:high   1.2424651  0.65305767 1.83187247 9.7e-08 ***\n",
       "Fe3:low-Fe3:high   1.1190579  0.52965051 1.70846531 1.7e-06 ***\n",
       "Fe2:mid-Fe3:high   0.9291210  0.33971365 1.51852845  0.0001 ***\n",
       "Fe3:mid-Fe3:high   0.7403013  0.15089389 1.32970869  0.0040 ** \n",
       "Fe3:low-Fe2:low   -0.1234072 -0.71281456 0.46600024  1.0000    \n",
       "Fe2:mid-Fe2:low   -0.3133440 -0.90275142 0.27606338  1.0000    \n",
       "Fe3:mid-Fe2:low   -0.5021638 -1.09157118 0.08724362  0.1785    \n",
       "Fe2:mid-Fe3:low   -0.1899369 -0.77934426 0.39947054  1.0000    \n",
       "Fe3:mid-Fe3:low   -0.3787566 -0.96816402 0.21065078  0.8427    \n",
       "Fe3:mid-Fe2:mid   -0.1888198 -0.77822716 0.40058764  1.0000    \n",
       "\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# install.packages(\"DescTools\")\n",
    "library(DescTools)\n",
    "PostHocTest(lnRetention.aov, method = \"bonferroni\")"
   ]
  }
 ],
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